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Last updated on 2026-10-06 11:54:43 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.2.7 | 23.92 | 267.37 | 291.29 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.2.7 | 14.25 | 186.23 | 200.48 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 1.2.7 | 18.00 | 190.37 | 208.37 | ERROR | |
| r-devel-linux-x86_64-fedora-gcc | 1.2.7 | 18.00 | 193.82 | 211.82 | ERROR | |
| r-devel-windows-x86_64 | 1.2.7 | 26.00 | 367.00 | 393.00 | OK | |
| r-patched-linux-x86_64 | 1.2.7 | 24.97 | 249.69 | 274.66 | OK | |
| r-release-linux-x86_64 | 1.2.7 | OK | ||||
| r-release-macos-arm64 | 1.2.7 | 6.00 | 92.00 | 98.00 | OK | |
| r-release-macos-x86_64 | 1.2.7 | 17.00 | 256.00 | 273.00 | OK | |
| r-release-windows-x86_64 | 1.2.7 | 24.00 | 353.00 | 377.00 | OK | |
| r-oldrel-macos-arm64 | 1.2.7 | 5.00 | 102.00 | 107.00 | OK | |
| r-oldrel-macos-x86_64 | 1.2.7 | 16.00 | 233.00 | 249.00 | OK | |
| r-oldrel-windows-x86_64 | 1.2.7 | 37.00 | 429.00 | 466.00 | OK |
Version: 1.2.7
Check: tests
Result: ERROR
Running ‘testthat.R’ [79s/117s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(rtemis)
.:rtemis 1.2.7 🌊 x86_64-pc-linux-gnu
> library(testthat)
Attaching package: 'testthat'
The following object is masked from 'package:rtemis':
describe
>
> test_check("rtemis")
Attaching package: 'data.table'
The following object is masked from 'package:base':
%notin%
2026-10-05 20:11:06 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:11:06 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:11:06 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 20:11:06 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:11:06 <1b>[0mUsing max n bins possible = 3.<1b>[0m [kfold]
2026-10-05 20:11:06 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Created file: /home/hornik/tmp/scratch/RtmpoTlsEM/rtemis_cluster.json<1b>[0m [write_lines]
2026-10-05 20:11:06 ✖ rtemis_range_error<1b>[0m [setup_KMeans]
2026-10-05 20:11:06 <1b>[0m▶<1b>[0m [cluster]
2026-10-05 20:11:06 <1b>[0mInput: 150 cases x 4 features.<1b>[0m [summarize_unsupervised]
2026-10-05 20:11:06 <1b>[0mClustering with KMeans...<1b>[0m [cluster]
2026-10-05 20:11:07 <1b>[0mChecking unsupervised data... ✔ [check_unsupervised_data]
2026-10-05 20:11:07 <1b>[0mClustering with KMeans ...<1b>[0m [cluster_]
2026-10-05 20:11:07 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.35 seconds.<1b>[0m [cluster]
2026-10-05 20:11:07 <1b>[0m▶<1b>[0m [cluster]
2026-10-05 20:11:07 <1b>[0mInput: 150 cases x 4 features.<1b>[0m [summarize_unsupervised]
2026-10-05 20:11:07 <1b>[0mClustering with KMeans...<1b>[0m [cluster]
2026-10-05 20:11:07 <1b>[0mChecking unsupervised data... ✔ [check_unsupervised_data]
2026-10-05 20:11:07 <1b>[0mClustering with KMeans ...<1b>[0m [cluster_]
2026-10-05 20:11:07 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.25 seconds.<1b>[0m [cluster]
2026-10-05 20:11:07 <1b>[0m▶<1b>[0m [cluster]
2026-10-05 20:11:07 <1b>[0mInput: 150 cases x 4 features.<1b>[0m [summarize_unsupervised]
2026-10-05 20:11:07 <1b>[0mClustering with HardCL...<1b>[0m [cluster]
2026-10-05 20:11:07 <1b>[0mChecking unsupervised data... ✔ [check_unsupervised_data]
2026-10-05 20:11:07 <1b>[0mClustering with HardCL ...<1b>[0m [cluster_]
2026-10-05 20:11:07 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.04 seconds.<1b>[0m [cluster]
2026-10-05 20:11:07 <1b>[0m▶<1b>[0m [cluster]
2026-10-05 20:11:07 <1b>[0mInput: 150 cases x 4 features.<1b>[0m [summarize_unsupervised]
2026-10-05 20:11:07 <1b>[0mClustering with NeuralGas...<1b>[0m [cluster]
2026-10-05 20:11:07 <1b>[0mChecking unsupervised data... ✔ [check_unsupervised_data]
2026-10-05 20:11:07 <1b>[0mClustering with NeuralGas ...<1b>[0m [cluster_]
2026-10-05 20:11:07 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.03 seconds.<1b>[0m [cluster]
2026-10-05 20:11:07 <1b>[0m▶<1b>[0m [cluster]
2026-10-05 20:11:07 <1b>[0mInput: 150 cases x 4 features.<1b>[0m [summarize_unsupervised]
2026-10-05 20:11:07 <1b>[0mClustering with CMeans...<1b>[0m [cluster]
2026-10-05 20:11:07 <1b>[0mChecking unsupervised data... ✔ [check_unsupervised_data]
2026-10-05 20:11:07 <1b>[0mClustering with CMeans ...<1b>[0m [cluster_]
Iteration: 1, Error: 1.0222276897
Iteration: 2, Error: 0.4426354825
Iteration: 3, Error: 0.4040115623
Iteration: 4, Error: 0.4035611242
Iteration: 5, Error: 0.4034484182
Iteration: 6, Error: 0.4034031102
Iteration: 7, Error: 0.4033845090
Iteration: 8, Error: 0.4033768332
Iteration: 9, Error: 0.4033736566
Iteration: 10, Error: 0.4033723396
Iteration: 11, Error: 0.4033717929
Iteration: 12, Error: 0.4033715658
Iteration: 13, Error: 0.4033714714
Iteration: 14, Error: 0.4033714321
Iteration: 15, Error: 0.4033714158
Iteration: 16, Error: 0.4033714090
Iteration: 17 converged, Error: 0.4033714062
2026-10-05 20:11:07 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.01 seconds.<1b>[0m [cluster]
2026-10-05 20:11:08 <1b>[0m▶<1b>[0m [cluster]
2026-10-05 20:11:08 <1b>[0mInput: 150 cases x 4 features.<1b>[0m [summarize_unsupervised]
2026-10-05 20:11:08 <1b>[0mClustering with DBSCAN...<1b>[0m [cluster]
2026-10-05 20:11:08 <1b>[0mChecking unsupervised data... ✔ [check_unsupervised_data]
2026-10-05 20:11:08 <1b>[0mClustering with DBSCAN ...<1b>[0m [cluster_]
Saving _problems/test_Clustering-100.R
2026-10-05 20:11:08 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Created file: /home/hornik/tmp/scratch/RtmpoTlsEM/rtemis_decompose.json<1b>[0m [write_lines]
2026-10-05 20:11:08 <1b>[0m▶<1b>[0m [decomp]
2026-10-05 20:11:08 <1b>[0mInput: 150 cases x 4 features.<1b>[0m [summarize_unsupervised]
2026-10-05 20:11:08 <1b>[0mDecomposing with PCA...<1b>[0m [decomp]
2026-10-05 20:11:08 <1b>[0mChecking unsupervised data... ✔ [check_unsupervised_data]
2026-10-05 20:11:08 <1b>[0mDecomposing with PCA ...<1b>[0m [decomp_]
2026-10-05 20:11:08 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.01 seconds.<1b>[0m [decomp]
2026-10-05 20:11:08 <1b>[0m▶<1b>[0m [decomp]
2026-10-05 20:11:08 <1b>[0mInput: 150 cases x 4 features.<1b>[0m [summarize_unsupervised]
2026-10-05 20:11:08 <1b>[0mDecomposing with ICA...<1b>[0m [decomp]
2026-10-05 20:11:08 <1b>[0mChecking unsupervised data... ✔ [check_unsupervised_data]
2026-10-05 20:11:08 <1b>[0mDecomposing with ICA ...<1b>[0m [decomp_]
Centering
colstandard
Whitening
Symmetric FastICA using logcosh approx. to neg-entropy function
Iteration 1 tol=0.038537
Iteration 2 tol=0.004207
Iteration 3 tol=0.002599
Iteration 4 tol=0.001640
Iteration 5 tol=0.000951
Iteration 6 tol=0.000483
Iteration 7 tol=0.000217
Iteration 8 tol=0.000088
2026-10-05 20:11:08 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.01 seconds.<1b>[0m [decomp]
2026-10-05 20:11:09 <1b>[0m▶<1b>[0m [decomp]
2026-10-05 20:11:09 <1b>[0mInput: 150 cases x 4 features.<1b>[0m [summarize_unsupervised]
2026-10-05 20:11:09 <1b>[0mDecomposing with NMF...<1b>[0m [decomp]
2026-10-05 20:11:09 <1b>[0mChecking unsupervised data... ✔ [check_unsupervised_data]
2026-10-05 20:11:09 <1b>[0mDecomposing with NMF ...<1b>[0m [decomp_]
2026-10-05 20:11:12 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 2.96 seconds.<1b>[0m [decomp]
2026-10-05 20:11:12 <1b>[0m▶<1b>[0m [decomp]
2026-10-05 20:11:12 <1b>[0mInput: 150 cases x 4 features.<1b>[0m [summarize_unsupervised]
2026-10-05 20:11:12 <1b>[0mDecomposing with UMAP...<1b>[0m [decomp]
2026-10-05 20:11:12 <1b>[0mChecking unsupervised data... ✔ [check_unsupervised_data]
2026-10-05 20:11:12 <1b>[0mDecomposing with UMAP ...<1b>[0m [decomp_]
2026-10-05 20:11:15 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 2.79 seconds.<1b>[0m [decomp]
2026-10-05 20:11:15 <1b>[0m▶<1b>[0m [decomp]
2026-10-05 20:11:15 <1b>[0mInput: 150 cases x 4 features.<1b>[0m [summarize_unsupervised]
2026-10-05 20:11:15 <1b>[0mDecomposing with UMAP...<1b>[0m [decomp]
2026-10-05 20:11:15 <1b>[0mChecking unsupervised data... ✔ [check_unsupervised_data]
2026-10-05 20:11:15 <1b>[0mDecomposing with UMAP ...<1b>[0m [decomp_]
2026-10-05 20:11:17 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 1.88 seconds.<1b>[0m [decomp]
2026-10-05 20:11:17 <1b>[0m▶<1b>[0m [decomp]
2026-10-05 20:11:17 <1b>[0mInput: 150 cases x 4 features.<1b>[0m [summarize_unsupervised]
2026-10-05 20:11:17 <1b>[0mDecomposing with tSNE...<1b>[0m [decomp]
2026-10-05 20:11:17 <1b>[0mChecking unsupervised data... ✔ [check_unsupervised_data]
2026-10-05 20:11:17 <1b>[0mDecomposing with tSNE ...<1b>[0m [decomp_]
2026-10-05 20:11:17 <1b>[0mRemoving 1 duplicate case...<1b>[0m [preprocess]
2026-10-05 20:11:17 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
2026-10-05 20:11:17 <1b>[0m▶<1b>[0m [decomp]
2026-10-05 20:11:17 <1b>[0mInput: 149 cases x 4 features.<1b>[0m [summarize_unsupervised]
2026-10-05 20:11:17 <1b>[0mDecomposing with tSNE...<1b>[0m [decomp]
2026-10-05 20:11:17 <1b>[0mChecking unsupervised data... ✔ [check_unsupervised_data]
2026-10-05 20:11:17 <1b>[0mDecomposing with tSNE ...<1b>[0m [decomp_]
2026-10-05 20:11:18 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.44 seconds.<1b>[0m [decomp]
2026-10-05 20:11:18 <1b>[0m▶<1b>[0m [decomp]
2026-10-05 20:11:18 <1b>[0mInput: 150 cases x 4 features.<1b>[0m [summarize_unsupervised]
2026-10-05 20:11:18 <1b>[0mDecomposing with Isomap...<1b>[0m [decomp]
2026-10-05 20:11:18 <1b>[0mChecking unsupervised data... ✔ [check_unsupervised_data]
2026-10-05 20:11:18 <1b>[0mDecomposing with Isomap ...<1b>[0m [decomp_]
2026-10-05 20:11:18 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.04 seconds.<1b>[0m [decomp]
2026-10-05 20:11:18 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:11:18 <1b>[0mUsing max n bins possible = 3.<1b>[0m [kfold]
2026-10-05 20:11:19 <1b>[0mScaling and centering 4 numeric features...<1b>[0m [preprocess]
2026-10-05 20:11:19 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
2026-10-05 20:11:19 <1b>[0mScaling and centering 4 numeric features...<1b>[0m [preprocess]
2026-10-05 20:11:19 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
2026-10-05 20:11:19 <1b>[0mScaling and centering 4 numeric features...<1b>[0m [preprocess]
2026-10-05 20:11:19 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
2026-10-05 20:11:19 <1b>[0mApplying preprocessing to test data...<1b>[0m [preprocess]
2026-10-05 20:11:19 <1b>[0mScaling and centering 4 numeric features...<1b>[0m [preprocess]
2026-10-05 20:11:19 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
2026-10-05 20:11:19 <1b>[0mImputing missing values using get_mode (discrete) and mean (continuous)...<1b>[0m [preprocess]
2026-10-05 20:11:19 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
2026-10-05 20:11:19 <1b>[0mOne hot encoding g... ✔ [one_hot]
2026-10-05 20:11:19 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
2026-10-05 20:11:19 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:11:19 <1b>[0mUsing max n bins possible = 3.<1b>[0m [kfold]
2026-10-05 20:11:19 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:11:19 <1b>[0mUsing max n bins possible = 3.<1b>[0m [kfold]
2026-10-05 20:11:19 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Created file: /home/hornik/tmp/scratch/RtmpoTlsEM/rtemis_super.json<1b>[0m [write_lines]
2026-10-05 20:11:19 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Created file: /home/hornik/tmp/scratch/RtmpoTlsEM/rtemis_decom.json<1b>[0m [write_lines]
2026-10-05 20:11:19 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Created file: /home/hornik/tmp/scratch/RtmpoTlsEM/rtemis_clust.json<1b>[0m [write_lines]
2026-10-05 20:11:19 ✖ rtemis_value_error<1b>[0m [.detect_config_kind]
2026-10-05 20:11:19 ✖ rtemis_value_error<1b>[0m [.detect_config_kind]
2026-10-05 20:11:20 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:11:20 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:11:20 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 20:11:20 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:11:20 <1b>[0mUsing max n bins possible = 3.<1b>[0m [kfold]
2026-10-05 20:11:20 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:20 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:20 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:20 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:20 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:11:20 <1b>[0mTraining GLM Regression...<1b>[0m [train]
2026-10-05 20:11:20 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLM<1b>[0m (Generalized Linear Model)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.73
<1b>[1m MSE<1b>[22m: 0.82
<1b>[1m RMSE<1b>[22m: 0.91
<1b>[1m R²<1b>[22m: 0.83
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.74
<1b>[1m MSE<1b>[22m: 1.03
<1b>[1m RMSE<1b>[22m: 1.01
<1b>[1m R²<1b>[22m: 0.77
2026-10-05 20:11:20 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.15 seconds.<1b>[0m [train]
2026-10-05 20:11:20 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:20 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:20 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:20 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:20 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:11:20 <1b>[0mTraining GLM Regression...<1b>[0m [train]
2026-10-05 20:11:20 <1b>[0mChecking data is ready for training...
2026-10-05 20:11:20 ✖ rtemis_missing_data<1b>[0m [check_supervised]
2026-10-05 20:11:20 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:20 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:20 <1b>[0mTraining set: 400 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:20 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: c(`_R_CHECK_LIMIT_CORES_` = 1) }<1b>[0m [get_n_workers]
2026-10-05 20:11:20 <1b>[0m<> Training GLM Regression using 3 independent folds...<1b>[0m [train]
2026-10-05 20:11:20 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:11:20 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Regression Model>
GLM (Generalized Linear Model)
⟳ Tested using 3 independent folds.
<Resampled Regression Training Metrics>
Showing mean (sd) across resamples.
MAE: 0.723 (0.010)
MSE: 0.833 (0.031)
RMSE: 0.913 (0.017)
R²: 0.830 (0.009)
<Resampled Regression Test Metrics>
Showing mean (sd) across resamples.
MAE: 0.747 (0.023)
MSE: 0.892 (0.065)
RMSE: 0.944 (0.034)
R²: 0.817 (0.017)
2026-10-05 20:11:21 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.33 seconds.<1b>[0m [train]
2026-10-05 20:11:21 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:21 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:21 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:21 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:21 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:11:21 <1b>[0mTraining GLM Classification...<1b>[0m [train]
2026-10-05 20:11:21 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLM<1b>[0m (Generalized Linear Model)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 44 1
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 44
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.978
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.998
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.018
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.093
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 20:11:21 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.19 seconds.<1b>[0m [train]
2026-10-05 20:11:21 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:21 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:21 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:21 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:21 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:11:21 <1b>[0mCalculating case weights using Inverse Frequency Weighting.<1b>[0m [ifw]
2026-10-05 20:11:21 <1b>[0mTraining GLM Classification...<1b>[0m [train]
2026-10-05 20:11:21 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLM<1b>[0m (Generalized Linear Model)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 44 1
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 44
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.978
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.998
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.018
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.093
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 20:11:21 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.11 seconds.<1b>[0m [train]
2026-10-05 20:11:21 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:21 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:21 <1b>[0mTraining set: 100 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:21 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: c(`_R_CHECK_LIMIT_CORES_` = 1) }<1b>[0m [get_n_workers]
2026-10-05 20:11:21 <1b>[0m<> Training GLM Classification using 3 independent folds...<1b>[0m [train]
2026-10-05 20:11:21 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:11:21 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 20:11:21 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Classification Model>
GLM (Generalized Linear Model)
⟳ Tested using 3 independent folds.
<Resampled Classification Training Metrics>
Aggregate Confusion Matrix across resamples.
Predicted
Reference virginica versicolor
virginica 99 1
versicolor 1 99
Showing mean (sd) across resamples.
Sensitivity: 0.990 (0.017)
Specificity: 0.990 (0.017)
Balanced Accuracy: 0.990 (0.017)
Ppv: 0.990 (0.017)
Npv: 0.990 (0.017)
F1: 0.990 (0.017)
Accuracy: 0.990 (0.017)
Auc: 0.998 (3.7e-03)
Brier Score: 0.007 (0.012)
<Resampled Classification Test Metrics>
Aggregate Confusion Matrix across resamples.
Predicted
Reference virginica versicolor
virginica 44 6
versicolor 5 45
Showing mean (sd) across resamples.
Sensitivity: 0.877 (0.125)
Specificity: 0.898 (0.095)
Balanced Accuracy: 0.888 (0.092)
Ppv: 0.898 (0.100)
Npv: 0.886 (0.118)
F1: 0.885 (0.096)
Accuracy: 0.888 (0.092)
Auc: 0.939 (0.062)
Brier Score: 0.101 (0.088)
2026-10-05 20:11:21 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.51 seconds.<1b>[0m [train]
2026-10-05 20:11:21 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:21 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:21 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:21 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:21 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:11:21 <1b>[0mTraining GLMNET Regression...<1b>[0m [train]
2026-10-05 20:11:21 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLMNET<1b>[0m (Elastic Net)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.73
<1b>[1m MSE<1b>[22m: 0.82
<1b>[1m RMSE<1b>[22m: 0.91
<1b>[1m R²<1b>[22m: 0.83
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.74
<1b>[1m MSE<1b>[22m: 1.02
<1b>[1m RMSE<1b>[22m: 1.01
<1b>[1m R²<1b>[22m: 0.77
2026-10-05 20:11:22 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.11 seconds.<1b>[0m [train]
2026-10-05 20:11:22 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:22 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:22 <1b>[0m▶ train GLMNET Regression<1b>[0m [session_render]
2026-10-05 20:11:22 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:22 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:22 <1b>[0m// Max workers: 2 { Algorithm: 1; Tuning: 2; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:11:22 <1b>[0m ▶ tune<1b>[0m [session_render]
2026-10-05 20:11:22 <1b>[0m▶<1b>[0m [tune_GridSearch]
2026-10-05 20:11:22 <1b>[0m<> Tuning GLMNET by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 20:11:22 <1b>[0m1 parameter combination x 5 resamples: 5 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 20:11:22 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mKFoldConfig<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m n<1b>[22m: 5
<1b>[1mstratify_var<1b>[22m: NULL
<1b>[1mstrat_n_bins<1b>[22m: 4
<1b>[1m id_strat<1b>[22m: NULL
<1b>[1m seed<1b>[22m: NULL
2026-10-05 20:11:22 <1b>[0mTuning using future (mirai_multisession); N workers: 2<1b>[0m [tune_GridSearch]
2026-10-05 20:11:22 ℹ Current future plan:<1b>[0m [tune_GridSearch]
mirai_multisession:
- args: function (..., workers = 2L, envir = parent.frame())
- tweaked: TRUE
- call: future::plan(strategy = requested_plan, workers = n_workers)
MiraiMultisessionFutureBackend:
Inherits: MiraiFutureBackend, MultiprocessFutureBackend, FutureBackend
UUID: b114d4355a5d106a7b0de46d745a1d44
Number of workers: 2
Number of free workers: 2
Available cores: 2
Automatic garbage collection: FALSE
Early signaling: FALSE
Interrupts are enabled: TRUE
Maximum total size of globals: +Inf
Maximum total size of value: +Inf
Number of active futures: 0
Number of futures since start: 0 (0 created, 0 launched, 0 finished)
Total runtime of futures: 0 secs (NaN secs/finished future)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLMNET<1b>[0m (Elastic Net)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.77
<1b>[1m MSE<1b>[22m: 0.94
<1b>[1m RMSE<1b>[22m: 0.97
<1b>[1m R²<1b>[22m: 0.81
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mValidation Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.78
<1b>[1m MSE<1b>[22m: 0.91
<1b>[1m RMSE<1b>[22m: 0.96
<1b>[1m R²<1b>[22m: 0.83
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLMNET<1b>[0m (Elastic Net)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.75
<1b>[1m MSE<1b>[22m: 0.90
<1b>[1m RMSE<1b>[22m: 0.95
<1b>[1m R²<1b>[22m: 0.82
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mValidation Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.75
<1b>[1m MSE<1b>[22m: 0.87
<1b>[1m RMSE<1b>[22m: 0.93
<1b>[1m R²<1b>[22m: 0.80
2026-10-05 20:11:24 ℹ Running grid line #1/5...<1b>[0m [tune_GridSearch]
2026-10-05 20:11:24 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:24 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:24 <1b>[0m Training set: 285 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:24 <1b>[0mValidation set: 73 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:24 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:11:24 <1b>[0mTraining GLMNET Regression...<1b>[0m [train]
2026-10-05 20:11:25 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:26 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 2.09 seconds.<1b>[0m [train]
2026-10-05 20:11:26 ℹ Running grid line #2/5...<1b>[0m [tune_GridSearch]
2026-10-05 20:11:26 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:26 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:26 <1b>[0m Training set: 288 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:26 <1b>[0mValidation set: 70 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:26 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:11:26 <1b>[0mTraining GLMNET Regression...<1b>[0m [train]
2026-10-05 20:11:26 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:26 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.17 seconds.<1b>[0m [train]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLMNET<1b>[0m (Elastic Net)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.78
<1b>[1m MSE<1b>[22m: 0.93
<1b>[1m RMSE<1b>[22m: 0.97
<1b>[1m R²<1b>[22m: 0.81
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mValidation Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.68
<1b>[1m MSE<1b>[22m: 0.80
<1b>[1m RMSE<1b>[22m: 0.89
<1b>[1m R²<1b>[22m: 0.83
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLMNET<1b>[0m (Elastic Net)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.73
<1b>[1m MSE<1b>[22m: 0.88
<1b>[1m RMSE<1b>[22m: 0.94
<1b>[1m R²<1b>[22m: 0.82
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mValidation Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.79
<1b>[1m MSE<1b>[22m: 0.85
<1b>[1m RMSE<1b>[22m: 0.92
<1b>[1m R²<1b>[22m: 0.83
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLMNET<1b>[0m (Elastic Net)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.73
<1b>[1m MSE<1b>[22m: 0.82
<1b>[1m RMSE<1b>[22m: 0.91
<1b>[1m R²<1b>[22m: 0.83
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mValidation Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.82
<1b>[1m MSE<1b>[22m: 1.14
<1b>[1m RMSE<1b>[22m: 1.07
<1b>[1m R²<1b>[22m: 0.79
2026-10-05 20:11:24 ℹ Running grid line #3/5...<1b>[0m [tune_GridSearch]
2026-10-05 20:11:24 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:24 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:24 <1b>[0m Training set: 286 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:24 <1b>[0mValidation set: 72 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:24 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:11:24 <1b>[0mTraining GLMNET Regression...<1b>[0m [train]
2026-10-05 20:11:25 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:25 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 1.72 seconds.<1b>[0m [train]
2026-10-05 20:11:25 ℹ Running grid line #4/5...<1b>[0m [tune_GridSearch]
2026-10-05 20:11:25 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:25 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:25 <1b>[0m Training set: 288 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:25 <1b>[0mValidation set: 70 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:25 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:11:25 <1b>[0mTraining GLMNET Regression...<1b>[0m [train]
2026-10-05 20:11:25 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:26 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.20 seconds.<1b>[0m [train]
2026-10-05 20:11:26 ℹ Running grid line #5/5...<1b>[0m [tune_GridSearch]
2026-10-05 20:11:26 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:26 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:26 <1b>[0m Training set: 285 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:26 <1b>[0mValidation set: 73 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:26 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:11:26 <1b>[0mTraining GLMNET Regression...<1b>[0m [train]
2026-10-05 20:11:26 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:26 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.11 seconds.<1b>[0m [train]
2026-10-05 20:11:26 ℹ Extracting best lambda from GLMNET models...<1b>[0m [tune_GridSearch]
2026-10-05 20:11:26 <1b>[0mBest config to minimize mse:<1b>[0m [tune_GridSearch]
lambda: {} => 0.140014948415186
2026-10-05 20:11:26 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 4.35 seconds.<1b>[0m [tune_GridSearch]
2026-10-05 20:11:26 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 20:11:26 <1b>[0m ✔ tune (4.6 s)<1b>[0m [session_render]
2026-10-05 20:11:26 <1b>[0mTraining GLMNET Regression with tuned hyperparameters...<1b>[0m [train]
2026-10-05 20:11:26 <1b>[0m ▶ train_alg GLMNET<1b>[0m [session_render]
2026-10-05 20:11:26 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:26 ℹ NCOL(xm): 6<1b>[0m [train_]
2026-10-05 20:11:26 ℹ Updated hyperparameters[["penalty_factor"]] to all 1s.<1b>[0m [train_]
2026-10-05 20:11:26 <1b>[0m ✔ train_alg GLMNET (92 ms)<1b>[0m [session_render]
2026-10-05 20:11:26 <1b>[0m ▶ predict<1b>[0m [session_render]
2026-10-05 20:11:26 <1b>[0m ✔ predict (11 ms)<1b>[0m [session_render]
2026-10-05 20:11:26 <1b>[0m ▶ varimp GLMNET<1b>[0m [session_render]
2026-10-05 20:11:26 <1b>[0m ✔ varimp GLMNET (7 ms)<1b>[0m [session_render]
2026-10-05 20:11:26 <1b>[0m ▶ metrics<1b>[0m [session_render]
2026-10-05 20:11:26 <1b>[0m ✔ metrics (53 ms)<1b>[0m [session_render]
2026-10-05 20:11:26 <1b>[0m✔ train GLMNET Regression (4.8 s)<1b>[0m [session_render]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLMNET<1b>[0m (Elastic Net)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.75
<1b>[1m MSE<1b>[22m: 0.90
<1b>[1m RMSE<1b>[22m: 0.95
<1b>[1m R²<1b>[22m: 0.82
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.73
<1b>[1m MSE<1b>[22m: 0.95
<1b>[1m RMSE<1b>[22m: 0.98
<1b>[1m R²<1b>[22m: 0.79
2026-10-05 20:11:26 <1b>[0mModels trained:<1b>[0m [session_report]
2026-10-05 20:11:26 <1b>[0m tuning: 1 combos ⨉ 5 inner resamples = 5<1b>[0m [session_report]
2026-10-05 20:11:26 <1b>[0m + final: 1 per fit = 1<1b>[0m [session_report]
2026-10-05 20:11:26 <1b>[0m total = 6 models<1b>[0m [session_report]
2026-10-05 20:11:26 <1b>[0m 6 succeeded in 4.8 s<1b>[0m [session_report]
2026-10-05 20:11:26 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 4.79 seconds.<1b>[0m [train]
2026-10-05 20:11:27 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:27 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:27 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:27 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:27 <1b>[0m// Max workers: 2 { Algorithm: 1; Tuning: 2; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:11:27 ✖ rtemis_value_error<1b>[0m [tune_GridSearch]
2026-10-05 20:11:27 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:27 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:27 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:27 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:27 <1b>[0m// Max workers: 2 { Algorithm: 1; Tuning: 2; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:11:27 <1b>[0m<> Tuning GLMNET by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 20:11:27 <1b>[0m1 parameter combination x 5 resamples: 5 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 20:11:27 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:11:27 <1b>[0mTuning using mirai; N workers: 2<1b>[0m [tune_GridSearch]
======>------------------------ 20% | ETA: 10s
==============================> 100% | ETA: 0s
2026-10-05 20:11:30 <1b>[0mBest config to minimize mse:<1b>[0m [tune_GridSearch]
lambda: {} => 0.15238782160033
2026-10-05 20:11:30 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 20:11:30 <1b>[0mTraining GLMNET Regression with tuned hyperparameters...<1b>[0m [train]
2026-10-05 20:11:30 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLMNET<1b>[0m (Elastic Net)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.76
<1b>[1m MSE<1b>[22m: 0.91
<1b>[1m RMSE<1b>[22m: 0.95
<1b>[1m R²<1b>[22m: 0.82
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.74
<1b>[1m MSE<1b>[22m: 0.95
<1b>[1m RMSE<1b>[22m: 0.97
<1b>[1m R²<1b>[22m: 0.79
2026-10-05 20:11:30 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 3.90 seconds.<1b>[0m [train]
2026-10-05 20:11:30 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:31 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:31 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:31 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:31 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:11:31 <1b>[0m<> Tuning GLMNET by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 20:11:31 <1b>[0m2 parameter combinations x 5 resamples: 10 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 20:11:31 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:11:31 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
2026-10-05 20:11:32 <1b>[0mBest config to minimize mse:<1b>[0m [tune_GridSearch]
alpha: {0, 1} => 1
lambda: {} => 0.134770240620737
2026-10-05 20:11:32 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 20:11:32 <1b>[0mTraining GLMNET Regression with tuned hyperparameters...<1b>[0m [train]
2026-10-05 20:11:32 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLMNET<1b>[0m (Elastic Net)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.75
<1b>[1m MSE<1b>[22m: 0.89
<1b>[1m RMSE<1b>[22m: 0.95
<1b>[1m R²<1b>[22m: 0.82
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.73
<1b>[1m MSE<1b>[22m: 0.96
<1b>[1m RMSE<1b>[22m: 0.98
<1b>[1m R²<1b>[22m: 0.79
2026-10-05 20:11:32 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 1.75 seconds.<1b>[0m [train]
2026-10-05 20:11:32 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:32 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:32 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:32 <1b>[0mTuning parallelization enabled.<1b>[0m [get_n_workers]
2026-10-05 20:11:32 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:11:32 <1b>[0m<> Training GLMNET Regression using 3 independent folds...<1b>[0m [train]
2026-10-05 20:11:32 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:11:37 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Regression Model>
GLMNET (Elastic Net)
⚙ Tuned using exhaustive grid search.
⟳ Tested using 3 independent folds.
<Resampled Regression Training Metrics>
Showing mean (sd) across resamples.
MAE: 0.760 (0.030)
MSE: 0.910 (0.042)
RMSE: 0.954 (0.022)
R²: 0.816 (0.011)
<Resampled Regression Test Metrics>
Showing mean (sd) across resamples.
MAE: 0.766 (0.056)
MSE: 0.921 (0.077)
RMSE: 0.959 (0.040)
R²: 0.813 (0.021)
2026-10-05 20:11:37 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 4.66 seconds.<1b>[0m [train]
2026-10-05 20:11:37 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:37 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:37 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:37 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:37 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:11:37 <1b>[0mCalculating case weights using Inverse Frequency Weighting.<1b>[0m [ifw]
2026-10-05 20:11:37 <1b>[0mTraining GLMNET Classification...<1b>[0m [train]
2026-10-05 20:11:37 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLMNET<1b>[0m (Elastic Net)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 43 2
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 2 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.956
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.998
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.024
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.055
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 20:11:37 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.06 seconds.<1b>[0m [train]
2026-10-05 20:11:37 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:37 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:37 <1b>[0mTraining set: 135 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:37 <1b>[0m Test set: 15 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:37 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:11:37 <1b>[0m<> Tuning GLMNET by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 20:11:37 <1b>[0m1 parameter combination x 5 resamples: 5 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 20:11:37 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:11:37 <1b>[0mUsing max n bins possible = 3.<1b>[0m [kfold]
2026-10-05 20:11:37 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
2026-10-05 20:11:39 <1b>[0mBest config to maximize balanced_accuracy:<1b>[0m [tune_GridSearch]
lambda: {} => 0.00821962912592355
2026-10-05 20:11:39 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 20:11:39 <1b>[0mCalculating case weights using Inverse Frequency Weighting.<1b>[0m [ifw]
2026-10-05 20:11:39 <1b>[0mTraining GLMNET Classification with tuned hyperparameters...<1b>[0m [train]
2026-10-05 20:11:39 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLMNET<1b>[0m (Elastic Net)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 45 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 42 3
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 2 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.963
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.963
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.963
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 0.933 0.956
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 0.978 0.967
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000 0.956 0.961
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 0.955 0.935
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 0.967 0.978
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000 0.944 0.945
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 5 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 5 0
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.933
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.933
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.933
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 1.000 0.800
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 0.900 1.000
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000 0.950 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 0.833 1.000
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 1.000 0.909
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000 0.909 0.889
2026-10-05 20:11:39 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 2.42 seconds.<1b>[0m [train]
2026-10-05 20:11:39 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:39 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:39 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:39 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:39 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:11:39 <1b>[0mTraining GAM Regression...<1b>[0m [train]
2026-10-05 20:11:39 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGAM<1b>[0m (Generalized Additive Model)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.72
<1b>[1m MSE<1b>[22m: 0.80
<1b>[1m RMSE<1b>[22m: 0.89
<1b>[1m R²<1b>[22m: 0.84
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.75
<1b>[1m MSE<1b>[22m: 1.01
<1b>[1m RMSE<1b>[22m: 1.00
<1b>[1m R²<1b>[22m: 0.77
2026-10-05 20:11:40 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.15 seconds.<1b>[0m [train]
2026-10-05 20:11:40 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:40 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:40 <1b>[0mTraining set: 358 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:40 <1b>[0m Test set: 42 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:40 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:11:40 <1b>[0mTraining GAM Regression...<1b>[0m [train]
2026-10-05 20:11:40 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGAM<1b>[0m (Generalized Additive Model)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 1.49
<1b>[1m MSE<1b>[22m: 2.95
<1b>[1m RMSE<1b>[22m: 1.72
<1b>[1m R²<1b>[22m: 0.40
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 1.26
<1b>[1m MSE<1b>[22m: 2.20
<1b>[1m RMSE<1b>[22m: 1.48
<1b>[1m R²<1b>[22m: 0.51
2026-10-05 20:11:40 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.16 seconds.<1b>[0m [train]
2026-10-05 20:11:40 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:40 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:40 <1b>[0mTraining set: 358 cases x 1 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:40 <1b>[0m Test set: 42 cases x 1 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:40 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:11:40 <1b>[0mTraining GAM Regression...<1b>[0m [train]
2026-10-05 20:11:40 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGAM<1b>[0m (Generalized Additive Model)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 1.37
<1b>[1m MSE<1b>[22m: 2.80
<1b>[1m RMSE<1b>[22m: 1.67
<1b>[1m R²<1b>[22m: 0.43
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 1.16
<1b>[1m MSE<1b>[22m: 1.93
<1b>[1m RMSE<1b>[22m: 1.39
<1b>[1m R²<1b>[22m: 0.57
2026-10-05 20:11:40 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.06 seconds.<1b>[0m [train]
2026-10-05 20:11:40 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:40 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:40 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:40 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:40 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: c(`_R_CHECK_LIMIT_CORES_` = 1); Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:11:40 <1b>[0m<> Tuning GAM by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 20:11:40 <1b>[0m3 parameter combinations x 5 resamples: 15 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 20:11:40 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:11:40 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
2026-10-05 20:11:42 <1b>[0mBest config to minimize mse:<1b>[0m [tune_GridSearch]
k: {3, 5, 7} => 3
2026-10-05 20:11:42 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 20:11:42 <1b>[0mTraining GAM Regression with tuned hyperparameters...<1b>[0m [train]
2026-10-05 20:11:42 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGAM<1b>[0m (Generalized Additive Model)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.72
<1b>[1m MSE<1b>[22m: 0.82
<1b>[1m RMSE<1b>[22m: 0.91
<1b>[1m R²<1b>[22m: 0.83
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.74
<1b>[1m MSE<1b>[22m: 1.02
<1b>[1m RMSE<1b>[22m: 1.01
<1b>[1m R²<1b>[22m: 0.77
2026-10-05 20:11:42 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 2.07 seconds.<1b>[0m [train]
2026-10-05 20:11:42 ✖ rtemis_dim_error<1b>[0m [predict_supervised_]
2026-10-05 20:11:42 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:42 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:42 <1b>[0mTraining set: 400 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:42 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: c(`_R_CHECK_LIMIT_CORES_` = 1) }<1b>[0m [get_n_workers]
2026-10-05 20:11:42 <1b>[0m<> Training GAM Regression using 3 independent folds...<1b>[0m [train]
2026-10-05 20:11:42 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:11:42 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Regression Model>
GAM (Generalized Additive Model)
⟳ Tested using 3 independent folds.
<Resampled Regression Training Metrics>
Showing mean (sd) across resamples.
MAE: 0.709 (0.025)
MSE: 0.796 (0.067)
RMSE: 0.891 (0.038)
R²: 0.838 (0.012)
<Resampled Regression Test Metrics>
Showing mean (sd) across resamples.
MAE: 0.759 (0.059)
MSE: 0.908 (0.136)
RMSE: 0.951 (0.070)
R²: 0.815 (0.024)
2026-10-05 20:11:42 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.41 seconds.<1b>[0m [train]
2026-10-05 20:11:42 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:42 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:42 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:42 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:42 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:11:42 <1b>[0mTraining GAM Classification...<1b>[0m [train]
2026-10-05 20:11:42 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGAM<1b>[0m (Generalized Additive Model)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 45 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 45
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Auc<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 2.2e-06
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.100
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 20:11:43 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.18 seconds.<1b>[0m [train]
2026-10-05 20:11:43 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:43 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:43 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:43 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:43 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:11:43 <1b>[0mCalculating case weights using Inverse Frequency Weighting.<1b>[0m [ifw]
2026-10-05 20:11:43 <1b>[0mTraining GAM Classification...<1b>[0m [train]
2026-10-05 20:11:43 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGAM<1b>[0m (Generalized Additive Model)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 45 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 45
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Auc<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 2.2e-06
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.100
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 20:11:43 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.18 seconds.<1b>[0m [train]
2026-10-05 20:11:43 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:43 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:43 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:43 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:43 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:11:43 <1b>[0mTraining LinearSVM Regression...<1b>[0m [train]
2026-10-05 20:11:43 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:43 <1b>[0mOne hot encoding g... ✔ [one_hot]
2026-10-05 20:11:43 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLinearSVM<1b>[0m (Support Vector Machine with Linear Kernel)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.72
<1b>[1m MSE<1b>[22m: 0.83
<1b>[1m RMSE<1b>[22m: 0.91
<1b>[1m R²<1b>[22m: 0.83
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.75
<1b>[1m MSE<1b>[22m: 1.02
<1b>[1m RMSE<1b>[22m: 1.01
<1b>[1m R²<1b>[22m: 0.77
2026-10-05 20:11:43 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.16 seconds.<1b>[0m [train]
2026-10-05 20:11:43 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:43 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:43 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:43 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:43 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:11:43 <1b>[0m<> Tuning LinearSVM by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 20:11:43 <1b>[0m2 parameter combinations x 5 resamples: 10 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 20:11:43 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:11:43 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
2026-10-05 20:11:46 <1b>[0mBest config to minimize mse:<1b>[0m [tune_GridSearch]
cost: {1, 10} => 1
2026-10-05 20:11:46 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 20:11:46 <1b>[0mTraining LinearSVM Regression with tuned hyperparameters...<1b>[0m [train]
2026-10-05 20:11:46 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:46 <1b>[0mOne hot encoding g... ✔ [one_hot]
2026-10-05 20:11:46 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLinearSVM<1b>[0m (Support Vector Machine with Linear Kernel)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.72
<1b>[1m MSE<1b>[22m: 0.83
<1b>[1m RMSE<1b>[22m: 0.91
<1b>[1m R²<1b>[22m: 0.83
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.75
<1b>[1m MSE<1b>[22m: 1.02
<1b>[1m RMSE<1b>[22m: 1.01
<1b>[1m R²<1b>[22m: 0.77
2026-10-05 20:11:46 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 2.90 seconds.<1b>[0m [train]
2026-10-05 20:11:46 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:46 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:46 <1b>[0mTraining set: 400 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:46 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: c(`_R_CHECK_LIMIT_CORES_` = 1) }<1b>[0m [get_n_workers]
2026-10-05 20:11:46 <1b>[0m<> Training LinearSVM Regression using 3 independent folds...<1b>[0m [train]
2026-10-05 20:11:46 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:11:46 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Regression Model>
LinearSVM (Support Vector Machine with Linear Kernel)
⟳ Tested using 3 independent folds.
<Resampled Regression Training Metrics>
Showing mean (sd) across resamples.
MAE: 0.723 (0.021)
MSE: 0.845 (0.022)
RMSE: 0.919 (0.012)
R²: 0.827 (0.008)
<Resampled Regression Test Metrics>
Showing mean (sd) across resamples.
MAE: 0.741 (0.043)
MSE: 0.875 (0.056)
RMSE: 0.935 (0.030)
R²: 0.821 (0.019)
2026-10-05 20:11:46 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.51 seconds.<1b>[0m [train]
2026-10-05 20:11:46 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:46 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:46 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:46 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:46 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:11:46 <1b>[0mTraining LinearSVM Classification...<1b>[0m [train]
2026-10-05 20:11:46 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:46 <1b>[0mOne hot encoding gn... ✔ [one_hot]
2026-10-05 20:11:46 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLinearSVM<1b>[0m (Support Vector Machine with Linear Kernel)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 45 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 2 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.957
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.999
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.023
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.041
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 20:11:47 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.12 seconds.<1b>[0m [train]
2026-10-05 20:11:47 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:47 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:47 <1b>[0mTraining set: 135 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:47 <1b>[0m Test set: 15 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:47 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:11:47 <1b>[0mTraining LinearSVM Classification...<1b>[0m [train]
2026-10-05 20:11:47 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLinearSVM<1b>[0m (Support Vector Machine with Linear Kernel)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 45 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 42 3
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 1 44
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.970
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.970
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.970
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 0.933 0.978
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 0.989 0.967
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000 0.961 0.972
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 0.977 0.936
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 0.967 0.989
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000 0.955 0.957
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 5 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 5 0
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 0 5
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 1.000
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000 1.000 1.000
2026-10-05 20:11:47 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.05 seconds.<1b>[0m [train]
2026-10-05 20:11:47 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:47 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:47 <1b>[0mTraining set: 100 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:47 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:11:47 <1b>[0m<> Training LinearSVM Classification using 3 independent folds...<1b>[0m [train]
2026-10-05 20:11:47 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:11:47 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 20:11:47 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Classification Model>
LinearSVM (Support Vector Machine with Linear Kernel)
⟳ Tested using 3 independent folds.
<Resampled Classification Training Metrics>
Aggregate Confusion Matrix across resamples.
Predicted
Reference virginica versicolor
virginica 100 0
versicolor 6 94
Showing mean (sd) across resamples.
Sensitivity: 1.000 (0.000)
Specificity: 0.940 (0.030)
Balanced Accuracy: 0.970 (0.015)
Ppv: 0.944 (0.027)
Npv: 1.000 (0.000)
F1: 0.971 (0.014)
Accuracy: 0.970 (0.015)
Auc: 0.998 (4.9e-04)
Brier Score: 0.029 (0.006)
<Resampled Classification Test Metrics>
Aggregate Confusion Matrix across resamples.
Predicted
Reference virginica versicolor
virginica 49 1
versicolor 3 47
Showing mean (sd) across resamples.
Sensitivity: 0.979 (0.036)
Specificity: 0.941 (0.059)
Balanced Accuracy: 0.960 (0.016)
Ppv: 0.946 (0.053)
Npv: 0.980 (0.034)
F1: 0.961 (0.015)
Accuracy: 0.960 (0.016)
Auc: 0.998 (4e-03)
Brier Score: 0.034 (1.7e-03)
2026-10-05 20:11:47 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.29 seconds.<1b>[0m [train]
2026-10-05 20:11:47 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:47 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:47 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:47 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:47 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:11:47 <1b>[0mTraining RadialSVM Regression...<1b>[0m [train]
2026-10-05 20:11:47 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:47 <1b>[0mOne hot encoding g... ✔ [one_hot]
2026-10-05 20:11:47 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mRadialSVM<1b>[0m (Support Vector Machine with Radial Kernel)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.73
<1b>[1m MSE<1b>[22m: 0.84
<1b>[1m RMSE<1b>[22m: 0.92
<1b>[1m R²<1b>[22m: 0.83
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.75
<1b>[1m MSE<1b>[22m: 1.01
<1b>[1m RMSE<1b>[22m: 1.01
<1b>[1m R²<1b>[22m: 0.77
2026-10-05 20:11:47 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.18 seconds.<1b>[0m [train]
2026-10-05 20:11:47 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:47 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:47 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:47 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:47 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: c(`_R_CHECK_LIMIT_CORES_` = 1); Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:11:47 <1b>[0m<> Tuning RadialSVM by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 20:11:47 <1b>[0m3 parameter combinations x 5 resamples: 15 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 20:11:47 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:11:47 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
2026-10-05 20:11:49 <1b>[0mBest config to minimize mse:<1b>[0m [tune_GridSearch]
cost: {1, 10, 100} => 1
2026-10-05 20:11:49 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 20:11:49 <1b>[0mTraining RadialSVM Regression with tuned hyperparameters...<1b>[0m [train]
2026-10-05 20:11:49 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:49 <1b>[0mOne hot encoding g... ✔ [one_hot]
2026-10-05 20:11:49 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mRadialSVM<1b>[0m (Support Vector Machine with Radial Kernel)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.73
<1b>[1m MSE<1b>[22m: 0.84
<1b>[1m RMSE<1b>[22m: 0.92
<1b>[1m R²<1b>[22m: 0.83
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.75
<1b>[1m MSE<1b>[22m: 1.01
<1b>[1m RMSE<1b>[22m: 1.01
<1b>[1m R²<1b>[22m: 0.77
2026-10-05 20:11:49 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 1.91 seconds.<1b>[0m [train]
2026-10-05 20:11:49 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:49 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:49 <1b>[0mTraining set: 400 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:49 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:11:49 <1b>[0m<> Training RadialSVM Regression using 3 independent folds...<1b>[0m [train]
2026-10-05 20:11:49 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:11:49 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Regression Model>
RadialSVM (Support Vector Machine with Radial Kernel)
⟳ Tested using 3 independent folds.
<Resampled Regression Training Metrics>
Showing mean (sd) across resamples.
MAE: 0.729 (0.014)
MSE: 0.861 (0.011)
RMSE: 0.928 (0.006)
R²: 0.824 (2.9e-03)
<Resampled Regression Test Metrics>
Showing mean (sd) across resamples.
MAE: 0.768 (0.032)
MSE: 0.938 (0.030)
RMSE: 0.968 (0.016)
R²: 0.809 (0.006)
2026-10-05 20:11:49 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.30 seconds.<1b>[0m [train]
2026-10-05 20:11:49 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:49 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:49 <1b>[0mTraining set: 400 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:49 <1b>[0mTuning parallelization enabled.<1b>[0m [get_n_workers]
2026-10-05 20:11:49 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:11:49 <1b>[0m<> Training RadialSVM Regression using 3 independent folds...<1b>[0m [train]
2026-10-05 20:11:49 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:11:54 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Regression Model>
RadialSVM (Support Vector Machine with Radial Kernel)
⚙ Tuned using exhaustive grid search.
⟳ Tested using 3 independent folds.
<Resampled Regression Training Metrics>
Showing mean (sd) across resamples.
MAE: 0.713 (0.024)
MSE: 0.837 (0.051)
RMSE: 0.915 (0.028)
R²: 0.829 (0.011)
<Resampled Regression Test Metrics>
Showing mean (sd) across resamples.
MAE: 0.787 (0.005)
MSE: 0.963 (3.8e-03)
RMSE: 0.981 (1.9e-03)
R²: 0.803 (0.007)
2026-10-05 20:11:54 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 4.58 seconds.<1b>[0m [train]
2026-10-05 20:11:54 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:54 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:54 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:54 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:54 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:11:54 <1b>[0mTraining RadialSVM Classification...<1b>[0m [train]
2026-10-05 20:11:54 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:54 <1b>[0mOne hot encoding gn... ✔ [one_hot]
2026-10-05 20:11:54 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mRadialSVM<1b>[0m (Support Vector Machine with Radial Kernel)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 42 3
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 2 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.933
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.944
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.955
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.935
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.944
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.944
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.995
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.035
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.059
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 20:11:54 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.09 seconds.<1b>[0m [train]
2026-10-05 20:11:54 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:54 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:54 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:54 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:54 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:11:54 <1b>[0m<> Tuning RadialSVM by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 20:11:54 <1b>[0m2 parameter combinations x 5 resamples: 10 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 20:11:54 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:11:54 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 20:11:54 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
2026-10-05 20:11:55 <1b>[0mBest config to maximize balanced_accuracy:<1b>[0m [tune_GridSearch]
cost: {1, 10} => 10
2026-10-05 20:11:55 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 20:11:55 <1b>[0mTraining RadialSVM Classification with tuned hyperparameters...<1b>[0m [train]
2026-10-05 20:11:55 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:55 <1b>[0mOne hot encoding gn... ✔ [one_hot]
2026-10-05 20:11:55 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mRadialSVM<1b>[0m (Support Vector Machine with Radial Kernel)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 44 1
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 2 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.967
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.957
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.977
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.967
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.967
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.999
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.022
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.049
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 20:11:55 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.99 seconds.<1b>[0m [train]
2026-10-05 20:11:55 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:55 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:55 <1b>[0mTraining set: 100 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:55 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:11:55 <1b>[0m<> Training RadialSVM Classification using 3 independent folds...<1b>[0m [train]
2026-10-05 20:11:55 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:11:55 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 20:11:55 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Classification Model>
RadialSVM (Support Vector Machine with Radial Kernel)
⟳ Tested using 3 independent folds.
<Resampled Classification Training Metrics>
Aggregate Confusion Matrix across resamples.
Predicted
Reference virginica versicolor
virginica 93 7
versicolor 4 96
Showing mean (sd) across resamples.
Sensitivity: 0.930 (0.018)
Specificity: 0.960 (0.045)
Balanced Accuracy: 0.945 (0.016)
Ppv: 0.961 (0.043)
Npv: 0.932 (0.014)
F1: 0.945 (0.015)
Accuracy: 0.945 (0.016)
Auc: 0.992 (4.2e-03)
Brier Score: 0.044 (0.008)
<Resampled Classification Test Metrics>
Aggregate Confusion Matrix across resamples.
Predicted
Reference virginica versicolor
virginica 45 5
versicolor 4 46
Showing mean (sd) across resamples.
Sensitivity: 0.901 (0.067)
Specificity: 0.920 (0.033)
Balanced Accuracy: 0.911 (0.028)
Ppv: 0.920 (0.027)
Npv: 0.906 (0.055)
F1: 0.909 (0.032)
Accuracy: 0.911 (0.028)
Auc: 0.979 (0.021)
Brier Score: 0.056 (0.020)
2026-10-05 20:11:55 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.25 seconds.<1b>[0m [train]
2026-10-05 20:11:55 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:55 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:55 <1b>[0mTraining set: 100 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:55 <1b>[0mTuning parallelization enabled.<1b>[0m [get_n_workers]
2026-10-05 20:11:55 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:11:55 <1b>[0m<> Training RadialSVM Classification using 3 independent folds...<1b>[0m [train]
2026-10-05 20:11:55 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:11:55 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 20:11:58 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Classification Model>
RadialSVM (Support Vector Machine with Radial Kernel)
⚙ Tuned using exhaustive grid search.
⟳ Tested using 3 independent folds.
<Resampled Classification Training Metrics>
Aggregate Confusion Matrix across resamples.
Predicted
Reference virginica versicolor
virginica 97 3
versicolor 2 98
Showing mean (sd) across resamples.
Sensitivity: 0.970 (5.1e-04)
Specificity: 0.980 (0.017)
Balanced Accuracy: 0.975 (0.009)
Ppv: 0.980 (0.017)
Npv: 0.970 (1e-03)
F1: 0.975 (0.009)
Accuracy: 0.975 (0.009)
Auc: 0.998 (1.4e-03)
Brier Score: 0.024 (0.006)
<Resampled Classification Test Metrics>
Aggregate Confusion Matrix across resamples.
Predicted
Reference virginica versicolor
virginica 47 3
versicolor 3 47
Showing mean (sd) across resamples.
Sensitivity: 0.941 (0.102)
Specificity: 0.939 (0.063)
Balanced Accuracy: 0.940 (0.029)
Ppv: 0.944 (0.056)
Npv: 0.950 (0.087)
F1: 0.939 (0.034)
Accuracy: 0.940 (0.029)
Auc: 0.989 (0.007)
Brier Score: 0.048 (0.007)
2026-10-05 20:11:58 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 2.31 seconds.<1b>[0m [train]
2026-10-05 20:11:58 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:58 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:58 <1b>[0mTraining set: 135 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:58 <1b>[0m Test set: 15 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:58 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:11:58 <1b>[0mTraining RadialSVM Classification...<1b>[0m [train]
2026-10-05 20:11:58 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mRadialSVM<1b>[0m (Support Vector Machine with Radial Kernel)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 45 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 39 6
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 6 39
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.911
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.911
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.911
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 0.867 0.867
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 0.933 0.933
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000 0.900 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 0.867 0.867
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 0.933 0.933
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000 0.867 0.867
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 5 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 5 0
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 2 3
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.867
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.861
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.867
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 1.000 0.600
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 0.800 1.000
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000 0.900 0.800
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 0.714 1.000
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 1.000 0.833
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000 0.833 0.750
2026-10-05 20:11:58 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.13 seconds.<1b>[0m [train]
2026-10-05 20:11:58 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:58 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:58 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:58 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:58 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:11:58 <1b>[0mTraining CART Regression...<1b>[0m [train]
2026-10-05 20:11:58 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mCART<1b>[0m (Classification and Regression Trees)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.84
<1b>[1m MSE<1b>[22m: 1.13
<1b>[1m RMSE<1b>[22m: 1.06
<1b>[1m R²<1b>[22m: 0.77
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 1.08
<1b>[1m MSE<1b>[22m: 1.80
<1b>[1m RMSE<1b>[22m: 1.34
<1b>[1m R²<1b>[22m: 0.60
2026-10-05 20:11:58 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.08 seconds.<1b>[0m [train]
2026-10-05 20:11:58 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:58 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:58 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:58 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:58 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:11:58 <1b>[0m<> Tuning CART by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 20:11:58 <1b>[0m2 parameter combinations x 5 resamples: 10 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 20:11:58 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:11:58 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
2026-10-05 20:11:59 <1b>[0mBest config to minimize mse:<1b>[0m [tune_GridSearch]
maxdepth: {2, 3} => 3
2026-10-05 20:11:59 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 20:11:59 <1b>[0mTraining CART Regression with tuned hyperparameters...<1b>[0m [train]
2026-10-05 20:11:59 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mCART<1b>[0m (Classification and Regression Trees)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.93
<1b>[1m MSE<1b>[22m: 1.43
<1b>[1m RMSE<1b>[22m: 1.19
<1b>[1m R²<1b>[22m: 0.71
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 1.05
<1b>[1m MSE<1b>[22m: 1.59
<1b>[1m RMSE<1b>[22m: 1.26
<1b>[1m R²<1b>[22m: 0.64
2026-10-05 20:11:59 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.55 seconds.<1b>[0m [train]
2026-10-05 20:11:59 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:59 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:59 <1b>[0mTraining set: 400 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:59 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:11:59 <1b>[0m<> Training CART Regression using 3 independent folds...<1b>[0m [train]
2026-10-05 20:11:59 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:11:59 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Regression Model>
CART (Classification and Regression Trees)
⟳ Tested using 3 independent folds.
<Resampled Regression Training Metrics>
Showing mean (sd) across resamples.
MAE: 0.788 (0.043)
MSE: 0.981 (0.063)
RMSE: 0.990 (0.032)
R²: 0.800 (0.011)
<Resampled Regression Test Metrics>
Showing mean (sd) across resamples.
MAE: 1.045 (0.051)
MSE: 1.710 (0.156)
RMSE: 1.307 (0.059)
R²: 0.651 (0.033)
2026-10-05 20:11:59 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.23 seconds.<1b>[0m [train]
2026-10-05 20:11:59 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:11:59 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:11:59 <1b>[0mTraining set: 400 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:11:59 <1b>[0mTuning parallelization enabled.<1b>[0m [get_n_workers]
2026-10-05 20:11:59 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:11:59 <1b>[0m<> Training CART Regression using 3 independent folds...<1b>[0m [train]
2026-10-05 20:11:59 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:12:02 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Regression Model>
CART (Classification and Regression Trees)
⚙ Tuned using exhaustive grid search.
⟳ Tested using 3 independent folds.
<Resampled Regression Training Metrics>
Showing mean (sd) across resamples.
MAE: 1.134 (0.054)
MSE: 1.944 (0.122)
RMSE: 1.394 (0.044)
R²: 0.604 (0.021)
<Resampled Regression Test Metrics>
Showing mean (sd) across resamples.
MAE: 1.223 (0.050)
MSE: 2.403 (0.145)
RMSE: 1.550 (0.047)
R²: 0.510 (0.025)
2026-10-05 20:12:02 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 3.48 seconds.<1b>[0m [train]
2026-10-05 20:12:02 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:02 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:12:02 <1b>[0mTraining set: 400 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:02 <1b>[0mTuning parallelization enabled.<1b>[0m [get_n_workers]
2026-10-05 20:12:02 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:12:02 <1b>[0m<> Training CART Regression using 3 independent folds...<1b>[0m [train]
2026-10-05 20:12:02 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:12:04 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Regression Model>
CART (Classification and Regression Trees)
⚙ Tuned using exhaustive grid search.
⟳ Tested using 3 independent folds.
<Resampled Regression Training Metrics>
Showing mean (sd) across resamples.
MAE: 0.804 (0.012)
MSE: 1.022 (0.027)
RMSE: 1.011 (0.013)
R²: 0.791 (0.011)
<Resampled Regression Test Metrics>
Showing mean (sd) across resamples.
MAE: 1.070 (0.022)
MSE: 1.810 (0.049)
RMSE: 1.345 (0.018)
R²: 0.629 (0.028)
2026-10-05 20:12:04 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 1.66 seconds.<1b>[0m [train]
2026-10-05 20:12:04 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:04 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:12:04 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:04 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:04 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: c(`_R_CHECK_LIMIT_CORES_` = 1); Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:12:04 <1b>[0m<> Tuning CART by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 20:12:04 <1b>[0m2 parameter combinations x 5 resamples: 10 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 20:12:04 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:12:04 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 20:12:04 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
2026-10-05 20:12:04 <1b>[0mBest config to maximize balanced_accuracy:<1b>[0m [tune_GridSearch]
maxdepth: {1, 2} => 2
2026-10-05 20:12:04 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 20:12:04 <1b>[0mTraining CART Classification with tuned hyperparameters...<1b>[0m [train]
2026-10-05 20:12:04 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mCART<1b>[0m (Classification and Regression Trees)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 45 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 2 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.957
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.987
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.020
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.096
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 20:12:05 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.48 seconds.<1b>[0m [train]
2026-10-05 20:12:05 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:05 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:12:05 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:05 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:05 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:12:05 <1b>[0mCalculating case weights using Inverse Frequency Weighting.<1b>[0m [ifw]
2026-10-05 20:12:05 <1b>[0mTraining CART Classification...<1b>[0m [train]
2026-10-05 20:12:05 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mCART<1b>[0m (Classification and Regression Trees)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 45 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 45
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Auc<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.000
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.100
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 20:12:05 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.08 seconds.<1b>[0m [train]
2026-10-05 20:12:05 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:05 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:12:05 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:05 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:05 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:12:05 <1b>[0m<> Tuning CART by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 20:12:05 <1b>[0m2 parameter combinations x 5 resamples: 10 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 20:12:05 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:12:05 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 20:12:05 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
2026-10-05 20:12:05 <1b>[0mBest config to maximize balanced_accuracy:<1b>[0m [tune_GridSearch]
maxdepth: {1, 2} => 2
2026-10-05 20:12:05 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 20:12:05 <1b>[0mTraining CART Classification with tuned hyperparameters...<1b>[0m [train]
2026-10-05 20:12:05 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mCART<1b>[0m (Classification and Regression Trees)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 45 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 2 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.957
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.987
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.020
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.096
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 20:12:05 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.56 seconds.<1b>[0m [train]
2026-10-05 20:12:05 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:05 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:12:05 <1b>[0mTraining set: 100 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:05 <1b>[0mTuning parallelization enabled.<1b>[0m [get_n_workers]
2026-10-05 20:12:05 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:12:05 <1b>[0m<> Training CART Classification using 3 independent folds...<1b>[0m [train]
2026-10-05 20:12:05 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:12:05 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 20:12:07 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Classification Model>
CART (Classification and Regression Trees)
⚙ Tuned using exhaustive grid search.
⟳ Tested using 3 independent folds.
<Resampled Classification Training Metrics>
Aggregate Confusion Matrix across resamples.
Predicted
Reference virginica versicolor
virginica 94 6
versicolor 5 95
Showing mean (sd) across resamples.
Sensitivity: 0.941 (0.059)
Specificity: 0.949 (0.063)
Balanced Accuracy: 0.945 (0.008)
Ppv: 0.954 (0.056)
Npv: 0.945 (0.053)
F1: 0.945 (0.008)
Accuracy: 0.945 (0.008)
Auc: 0.946 (0.008)
Brier Score: 0.050 (0.006)
<Resampled Classification Test Metrics>
Aggregate Confusion Matrix across resamples.
Predicted
Reference virginica versicolor
virginica 46 4
versicolor 4 46
Showing mean (sd) across resamples.
Sensitivity: 0.920 (0.089)
Specificity: 0.920 (0.089)
Balanced Accuracy: 0.920 (0.015)
Ppv: 0.929 (0.075)
Npv: 0.929 (0.075)
F1: 0.920 (0.017)
Accuracy: 0.920 (0.015)
Auc: 0.911 (0.028)
Brier Score: 0.075 (0.011)
2026-10-05 20:12:07 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 1.60 seconds.<1b>[0m [train]
2026-10-05 20:12:07 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:07 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:12:07 <1b>[0mTraining set: 135 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:07 <1b>[0m Test set: 15 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:07 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:12:07 <1b>[0mTraining CART Classification...<1b>[0m [train]
2026-10-05 20:12:07 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mCART<1b>[0m (Classification and Regression Trees)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 45 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 44 1
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 0 45
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.993
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.993
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.993
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 0.978 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 1.000 0.989
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000 0.989 0.994
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 1.000 0.978
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 0.989 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000 0.989 0.989
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 5 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 5 0
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 0 5
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 1.000
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000 1.000 1.000
2026-10-05 20:12:07 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.07 seconds.<1b>[0m [train]
2026-10-05 20:12:07 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:07 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:12:07 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:07 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:07 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:12:07 <1b>[0mTraining LightCART Regression...<1b>[0m [train]
2026-10-05 20:12:07 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:07 <1b>[0mConverting 1 factor to integer...<1b>[0m [preprocess]
2026-10-05 20:12:07 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightCART<1b>[0m (Decision Tree)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 1.66
<1b>[1m MSE<1b>[22m: 4.24
<1b>[1m RMSE<1b>[22m: 2.06
<1b>[1m R²<1b>[22m: 0.15
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 1.58
<1b>[1m MSE<1b>[22m: 3.75
<1b>[1m RMSE<1b>[22m: 1.94
<1b>[1m R²<1b>[22m: 0.16
2026-10-05 20:12:07 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.18 seconds.<1b>[0m [train]
2026-10-05 20:12:07 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:07 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:12:07 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:07 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:07 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:12:07 <1b>[0mTraining LightCART Regression...<1b>[0m [train]
2026-10-05 20:12:07 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:07 <1b>[0mConverting 1 factor to integer...<1b>[0m [preprocess]
2026-10-05 20:12:07 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightCART<1b>[0m (Decision Tree)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 1.66
<1b>[1m MSE<1b>[22m: 4.24
<1b>[1m RMSE<1b>[22m: 2.06
<1b>[1m R²<1b>[22m: 0.15
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 1.58
<1b>[1m MSE<1b>[22m: 3.75
<1b>[1m RMSE<1b>[22m: 1.94
<1b>[1m R²<1b>[22m: 0.16
2026-10-05 20:12:07 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.11 seconds.<1b>[0m [train]
2026-10-05 20:12:07 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:07 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:12:07 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:07 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:07 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:12:07 <1b>[0mTraining LightCART Classification...<1b>[0m [train]
2026-10-05 20:12:07 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:07 <1b>[0mConverting 2 factors to integer...<1b>[0m [preprocess]
2026-10-05 20:12:07 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightCART<1b>[0m (Decision Tree)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 41 4
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 44
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.911
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.944
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.976
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.917
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.943
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.944
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.211
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.860
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.219
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 20:12:07 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.07 seconds.<1b>[0m [train]
2026-10-05 20:12:07 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:07 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:12:07 <1b>[0mTraining set: 135 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:07 <1b>[0m Test set: 15 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:07 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:12:07 <1b>[0mTraining LightCART Classification...<1b>[0m [train]
2026-10-05 20:12:07 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:07 <1b>[0mConverting 1 factor to integer...<1b>[0m [preprocess]
2026-10-05 20:12:07 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightCART<1b>[0m (Decision Tree)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 44 1 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 43 2
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 3 42
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.956
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.956
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.978 0.956 0.933
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 0.956 0.978
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.989 0.956 0.956
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 0.915 0.955
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.989 0.977 0.967
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.989 0.935 0.944
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 5 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 5 0
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.933
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.933
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.933
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 1.000 0.800
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 0.900 1.000
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000 0.950 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 0.833 1.000
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 1.000 0.909
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000 0.909 0.889
2026-10-05 20:12:07 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.10 seconds.<1b>[0m [train]
2026-10-05 20:12:07 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:07 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:12:07 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:07 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:07 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:12:07 <1b>[0mTraining LightRF Regression...<1b>[0m [train]
2026-10-05 20:12:07 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:07 <1b>[0mConverting 1 factor to integer...<1b>[0m [preprocess]
2026-10-05 20:12:07 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightRF<1b>[0m (LightGBM Random Forest)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.92
<1b>[1m MSE<1b>[22m: 1.34
<1b>[1m RMSE<1b>[22m: 1.16
<1b>[1m R²<1b>[22m: 0.73
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.89
<1b>[1m MSE<1b>[22m: 1.18
<1b>[1m RMSE<1b>[22m: 1.09
<1b>[1m R²<1b>[22m: 0.73
2026-10-05 20:12:08 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.25 seconds.<1b>[0m [train]
2026-10-05 20:12:08 <1b>[0mConverting 1 factor to integer...<1b>[0m [preprocess]
2026-10-05 20:12:08 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
2026-10-05 20:12:08 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:08 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:12:08 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:08 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:08 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:12:08 <1b>[0m<> Tuning LightRF by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 20:12:08 <1b>[0m2 parameter combinations x 5 resamples: 10 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 20:12:08 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:12:08 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
2026-10-05 20:12:09 <1b>[0mBest config to minimize mse:<1b>[0m [tune_GridSearch]
lambda_l1: {0, 0.1} => 0
2026-10-05 20:12:09 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 20:12:09 <1b>[0mTraining LightRF Regression with tuned hyperparameters...<1b>[0m [train]
2026-10-05 20:12:09 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:09 <1b>[0mConverting 1 factor to integer...<1b>[0m [preprocess]
2026-10-05 20:12:09 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightRF<1b>[0m (LightGBM Random Forest)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.92
<1b>[1m MSE<1b>[22m: 1.33
<1b>[1m RMSE<1b>[22m: 1.15
<1b>[1m R²<1b>[22m: 0.73
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.88
<1b>[1m MSE<1b>[22m: 1.17
<1b>[1m RMSE<1b>[22m: 1.08
<1b>[1m R²<1b>[22m: 0.74
2026-10-05 20:12:09 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 1.50 seconds.<1b>[0m [train]
2026-10-05 20:12:09 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:09 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:12:09 <1b>[0mTraining set: 400 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:09 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:12:09 <1b>[0m<> Training LightRF Regression using 3 independent folds...<1b>[0m [train]
2026-10-05 20:12:09 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:12:13 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Regression Model>
LightRF (LightGBM Random Forest)
⚙ Tuned using exhaustive grid search.
⟳ Tested using 3 independent folds.
<Resampled Regression Training Metrics>
Showing mean (sd) across resamples.
MAE: 0.962 (0.025)
MSE: 1.446 (0.126)
RMSE: 1.202 (0.053)
R²: 0.705 (0.030)
<Resampled Regression Test Metrics>
Showing mean (sd) across resamples.
MAE: 1.035 (0.067)
MSE: 1.657 (0.266)
RMSE: 1.284 (0.103)
R²: 0.661 (0.064)
2026-10-05 20:12:13 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 3.95 seconds.<1b>[0m [train]
2026-10-05 20:12:13 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:13 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:12:13 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:13 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:13 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:12:13 <1b>[0mTraining LightRF Classification...<1b>[0m [train]
2026-10-05 20:12:13 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:13 <1b>[0mConverting 2 factors to integer...<1b>[0m [preprocess]
2026-10-05 20:12:13 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightRF<1b>[0m (LightGBM Random Forest)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 41 4
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 44
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.911
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.944
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.976
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.917
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.943
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.944
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.996
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.048
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.092
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 20:12:13 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.12 seconds.<1b>[0m [train]
2026-10-05 20:12:13 <1b>[0mConverting 1 factor to integer...<1b>[0m [preprocess]
2026-10-05 20:12:13 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
2026-10-05 20:12:13 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:13 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:12:13 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:13 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:13 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:12:13 <1b>[0m<> Tuning LightRF by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 20:12:13 <1b>[0m2 parameter combinations x 5 resamples: 10 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 20:12:13 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:12:13 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 20:12:13 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
2026-10-05 20:12:15 <1b>[0mBest config to maximize balanced_accuracy:<1b>[0m [tune_GridSearch]
max_depth: {-1, 5} => -1
2026-10-05 20:12:15 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 20:12:15 <1b>[0mTraining LightRF Classification with tuned hyperparameters...<1b>[0m [train]
2026-10-05 20:12:15 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:15 <1b>[0mConverting 2 factors to integer...<1b>[0m [preprocess]
2026-10-05 20:12:15 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightRF<1b>[0m (LightGBM Random Forest)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 41 4
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 44
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.911
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.944
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.976
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.917
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.943
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.944
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.996
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.048
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.092
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 20:12:15 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 1.46 seconds.<1b>[0m [train]
2026-10-05 20:12:15 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:15 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:12:15 <1b>[0mTraining set: 100 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:15 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:12:15 <1b>[0m<> Training LightRF Classification using 3 independent folds...<1b>[0m [train]
2026-10-05 20:12:15 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:12:15 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 20:12:16 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Classification Model>
LightRF (LightGBM Random Forest)
⟳ Tested using 3 independent folds.
<Resampled Classification Training Metrics>
Aggregate Confusion Matrix across resamples.
Predicted
Reference virginica versicolor
virginica 95 5
versicolor 4 96
Showing mean (sd) across resamples.
Sensitivity: 0.950 (0.018)
Specificity: 0.960 (0.045)
Balanced Accuracy: 0.955 (0.014)
Ppv: 0.962 (0.042)
Npv: 0.951 (0.015)
F1: 0.955 (0.013)
Accuracy: 0.955 (0.014)
Auc: 0.988 (0.013)
Brier Score: 0.111 (0.022)
<Resampled Classification Test Metrics>
Aggregate Confusion Matrix across resamples.
Predicted
Reference virginica versicolor
virginica 45 5
versicolor 3 47
Showing mean (sd) across resamples.
Sensitivity: 0.901 (0.067)
Specificity: 0.941 (0.102)
Balanced Accuracy: 0.921 (0.044)
Ppv: 0.947 (0.091)
Npv: 0.908 (0.051)
F1: 0.920 (0.042)
Accuracy: 0.921 (0.044)
Auc: 0.976 (0.030)
Brier Score: 0.115 (0.038)
2026-10-05 20:12:16 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.76 seconds.<1b>[0m [train]
2026-10-05 20:12:16 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:16 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:12:16 <1b>[0mTraining set: 135 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:16 <1b>[0m Test set: 15 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:16 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:12:16 <1b>[0mTraining LightRF Classification...<1b>[0m [train]
2026-10-05 20:12:16 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:16 <1b>[0mConverting 1 factor to integer...<1b>[0m [preprocess]
2026-10-05 20:12:16 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightRF<1b>[0m (LightGBM Random Forest)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 45 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 42 2
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 3 42
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.956
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.955
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.956
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 0.933 0.933
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.989 0.967 0.978
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.994 0.950 0.956
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.978 0.933 0.955
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 0.967 0.967
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.989 0.933 0.944
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 5 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 5 0
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.933
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.933
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.933
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 1.000 0.800
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 0.900 1.000
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000 0.950 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 0.833 1.000
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 1.000 0.909
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000 0.909 0.889
2026-10-05 20:12:16 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.42 seconds.<1b>[0m [train]
2026-10-05 20:12:16 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:16 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:12:16 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:16 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:16 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:12:16 <1b>[0mTraining LightGBM Regression...<1b>[0m [train]
2026-10-05 20:12:16 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:16 <1b>[0mConverting 1 factor to integer...<1b>[0m [preprocess]
2026-10-05 20:12:16 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightGBM<1b>[0m (Gradient Boosting)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 1.29
<1b>[1m MSE<1b>[22m: 2.63
<1b>[1m RMSE<1b>[22m: 1.62
<1b>[1m R²<1b>[22m: 0.47
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 1.20
<1b>[1m MSE<1b>[22m: 2.33
<1b>[1m RMSE<1b>[22m: 1.53
<1b>[1m R²<1b>[22m: 0.48
2026-10-05 20:12:16 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.33 seconds.<1b>[0m [train]
2026-10-05 20:12:16 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:16 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:12:16 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:16 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:16 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:12:16 <1b>[0m<> Tuning LightGBM by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 20:12:16 <1b>[0m1 parameter combination x 5 resamples: 5 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 20:12:16 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:12:16 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
2026-10-05 20:12:18 <1b>[0mBest config to minimize mse:<1b>[0m [tune_GridSearch]
nrounds: {} => 434
2026-10-05 20:12:18 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 20:12:18 <1b>[0mTraining LightGBM Regression with tuned hyperparameters...<1b>[0m [train]
2026-10-05 20:12:18 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:18 <1b>[0mConverting 1 factor to integer...<1b>[0m [preprocess]
2026-10-05 20:12:18 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightGBM<1b>[0m (Gradient Boosting)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.60
<1b>[1m MSE<1b>[22m: 0.58
<1b>[1m RMSE<1b>[22m: 0.76
<1b>[1m R²<1b>[22m: 0.88
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.80
<1b>[1m MSE<1b>[22m: 1.19
<1b>[1m RMSE<1b>[22m: 1.09
<1b>[1m R²<1b>[22m: 0.73
2026-10-05 20:12:20 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 3.77 seconds.<1b>[0m [train]
2026-10-05 20:12:20 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:20 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:12:20 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:20 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:12:20 <1b>[0m<> Training LightGBM Regression using 3 independent folds...<1b>[0m [train]
2026-10-05 20:12:20 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:12:22 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Regression Model>
LightGBM (Gradient Boosting)
⚙ Tuned using exhaustive grid search.
⟳ Tested using 3 independent folds.
<Resampled Regression Training Metrics>
Showing mean (sd) across resamples.
MAE: 1.287 (0.027)
MSE: 2.609 (0.113)
RMSE: 1.615 (0.035)
R²: 0.473 (0.006)
<Resampled Regression Test Metrics>
Showing mean (sd) across resamples.
MAE: 1.336 (0.047)
MSE: 2.820 (0.253)
RMSE: 1.678 (0.075)
R²: 0.429 (0.020)
2026-10-05 20:12:22 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 2.05 seconds.<1b>[0m [train]
2026-10-05 20:12:22 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:22 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:12:22 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:22 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:22 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:12:22 <1b>[0m<> Tuning LightGBM by exhaustive grid search with 3 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 20:12:22 <1b>[0m1 parameter combination x 3 resamples: 3 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 20:12:22 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:12:22 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 20:12:22 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
2026-10-05 20:12:23 <1b>[0mBest config to maximize balanced_accuracy:<1b>[0m [tune_GridSearch]
nrounds: {} => 375
2026-10-05 20:12:23 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 20:12:23 <1b>[0mTraining LightGBM Classification with tuned hyperparameters...<1b>[0m [train]
2026-10-05 20:12:23 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:23 <1b>[0mConverting 2 factors to integer...<1b>[0m [preprocess]
2026-10-05 20:12:23 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightGBM<1b>[0m (Gradient Boosting)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 45 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 2 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.957
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.999
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.017
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.097
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 20:12:24 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 1.85 seconds.<1b>[0m [train]
2026-10-05 20:12:24 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:24 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:12:24 <1b>[0mTraining set: 135 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:24 <1b>[0m Test set: 15 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:24 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:12:24 <1b>[0mTraining LightGBM Classification...<1b>[0m [train]
2026-10-05 20:12:24 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:24 <1b>[0mConverting 1 factor to integer...<1b>[0m [preprocess]
2026-10-05 20:12:24 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightGBM<1b>[0m (Gradient Boosting)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 45 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 43 2
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 3 42
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.963
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.963
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.963
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 0.956 0.933
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 0.967 0.978
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000 0.961 0.956
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 0.935 0.955
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 0.978 0.967
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000 0.945 0.944
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 5 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 5 0
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.933
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.933
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.933
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 1.000 0.800
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 0.900 1.000
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000 0.950 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 0.833 1.000
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 1.000 0.909
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000 0.909 0.889
2026-10-05 20:12:24 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.29 seconds.<1b>[0m [train]
2026-10-05 20:12:24 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:24 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:12:24 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:24 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:24 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:12:24 <1b>[0mTraining LightRuleFit Regression...<1b>[0m [train]
2026-10-05 20:12:25 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:25 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:25 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:12:25 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:25 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:12:25 <1b>[0mTraining LightGBM Regression...<1b>[0m [train]
2026-10-05 20:12:25 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:25 <1b>[0mConverting 1 factor to integer...<1b>[0m [preprocess]
2026-10-05 20:12:25 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightGBM<1b>[0m (Gradient Boosting)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.78
<1b>[1m MSE<1b>[22m: 0.96
<1b>[1m RMSE<1b>[22m: 0.98
<1b>[1m R²<1b>[22m: 0.81
2026-10-05 20:12:25 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.24 seconds.<1b>[0m [train]
2026-10-05 20:12:25 <1b>[0mExtracting LightGBM rules... ✔ [extract_rules]
2026-10-05 20:12:25 <1b>[0mExtracted 180 unique rules.<1b>[0m [extract_rules]
2026-10-05 20:12:25 <1b>[0mMatching180rules to358cases... ✔ [match_cases_by_rules]
2026-10-05 20:12:25 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:25 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:12:25 <1b>[0mTraining set: 358 cases x 180 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:25 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:12:25 <1b>[0mTraining GLMNET Regression...<1b>[0m [train]
2026-10-05 20:12:25 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLMNET<1b>[0m (Elastic Net)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.64
<1b>[1m MSE<1b>[22m: 0.65
<1b>[1m RMSE<1b>[22m: 0.80
<1b>[1m R²<1b>[22m: 0.87
2026-10-05 20:12:25 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.10 seconds.<1b>[0m [train]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightRuleFit<1b>[0m (LightGBM RuleFit)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.64
<1b>[1m MSE<1b>[22m: 0.65
<1b>[1m RMSE<1b>[22m: 0.80
<1b>[1m R²<1b>[22m: 0.87
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.82
<1b>[1m MSE<1b>[22m: 1.25
<1b>[1m RMSE<1b>[22m: 1.12
<1b>[1m R²<1b>[22m: 0.72
2026-10-05 20:12:25 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.81 seconds.<1b>[0m [train]
2026-10-05 20:12:25 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:25 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:12:25 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:25 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:25 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:12:25 <1b>[0mTraining LightRuleFit Classification...<1b>[0m [train]
2026-10-05 20:12:25 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:25 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:25 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:12:25 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:25 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:12:25 <1b>[0mTraining LightGBM Classification...<1b>[0m [train]
2026-10-05 20:12:25 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:25 <1b>[0mConverting 2 factors to integer...<1b>[0m [preprocess]
2026-10-05 20:12:25 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightGBM<1b>[0m (Gradient Boosting)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 45 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 2 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.957
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.998
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.019
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 20:12:26 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.34 seconds.<1b>[0m [train]
2026-10-05 20:12:26 <1b>[0mExtracting LightGBM rules... ✔ [extract_rules]
2026-10-05 20:12:26 <1b>[0mExtracted 12 unique rules.<1b>[0m [extract_rules]
2026-10-05 20:12:26 <1b>[0mMatching12rules to90cases... ✔ [match_cases_by_rules]
2026-10-05 20:12:26 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:26 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:12:26 <1b>[0mTraining set: 90 cases x 12 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:26 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:12:26 <1b>[0m<> Tuning GLMNET by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 20:12:26 <1b>[0m1 parameter combination x 5 resamples: 5 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 20:12:26 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:12:26 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 20:12:26 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
2026-10-05 20:12:27 <1b>[0mBest config to maximize balanced_accuracy:<1b>[0m [tune_GridSearch]
lambda: {} => 0.0658148865916772
2026-10-05 20:12:27 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 20:12:27 <1b>[0mCalculating case weights using Inverse Frequency Weighting.<1b>[0m [ifw]
2026-10-05 20:12:27 <1b>[0mTraining GLMNET Classification with tuned hyperparameters...<1b>[0m [train]
2026-10-05 20:12:27 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLMNET<1b>[0m (Elastic Net)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 45 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 2 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.957
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.996
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.028
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 20:12:27 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 1.06 seconds.<1b>[0m [train]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightRuleFit<1b>[0m (LightGBM RuleFit)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 45 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 2 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.957
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.996
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.028
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.094
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 20:12:27 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 1.69 seconds.<1b>[0m [train]
2026-10-05 20:12:30 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:30 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:12:30 <1b>[0mTraining set: 50 cases x 1 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:30 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:12:30 <1b>[0mTraining Isotonic Regression...<1b>[0m [train]
2026-10-05 20:12:30 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mIsotonic<1b>[0m (Isotonic Regression)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.51
<1b>[1m MSE<1b>[22m: 0.61
<1b>[1m RMSE<1b>[22m: 0.78
<1b>[1m R²<1b>[22m: 0.99
2026-10-05 20:12:30 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.07 seconds.<1b>[0m [train]
2026-10-05 20:12:30 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:30 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:12:30 <1b>[0mTraining set: 200 cases x 1 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:30 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:12:30 <1b>[0mTraining Isotonic Classification...<1b>[0m [train]
2026-10-05 20:12:30 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mIsotonic<1b>[0m (Isotonic Regression)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mb <1b>[0m<1b>[1;38;2;108;163;160ma <1b>[0m
<1b>[1;38;2;108;163;160m b<1b>[0m 90 6
<1b>[1;38;2;108;163;160m a<1b>[0m 12 92
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.938
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.885
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.911
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.882
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.939
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.910
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.057
Positive Class <1b>[1;38;2;108;163;160mb<1b>[0m
2026-10-05 20:12:30 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.10 seconds.<1b>[0m [train]
2026-10-05 20:12:30 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:30 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:12:30 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:30 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:30 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:12:30 <1b>[0mTraining Ranger Regression...<1b>[0m [train]
2026-10-05 20:12:30 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mRanger<1b>[0m (Random Forest)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.40
<1b>[1m MSE<1b>[22m: 0.26
<1b>[1m RMSE<1b>[22m: 0.51
<1b>[1m R²<1b>[22m: 0.95
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.89
<1b>[1m MSE<1b>[22m: 1.29
<1b>[1m RMSE<1b>[22m: 1.13
<1b>[1m R²<1b>[22m: 0.71
2026-10-05 20:12:30 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.16 seconds.<1b>[0m [train]
2026-10-05 20:12:30 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:30 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:12:30 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:30 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:30 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:12:30 <1b>[0m<> Tuning Ranger by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 20:12:30 <1b>[0m2 parameter combinations x 5 resamples: 10 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 20:12:30 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:12:30 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
2026-10-05 20:12:31 <1b>[0mBest config to minimize mse:<1b>[0m [tune_GridSearch]
mtry: {3, 6} => 6
2026-10-05 20:12:32 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 20:12:32 <1b>[0mTraining Ranger Regression with tuned hyperparameters...<1b>[0m [train]
2026-10-05 20:12:32 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mRanger<1b>[0m (Random Forest)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.34
<1b>[1m MSE<1b>[22m: 0.20
<1b>[1m RMSE<1b>[22m: 0.44
<1b>[1m R²<1b>[22m: 0.96
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.91
<1b>[1m MSE<1b>[22m: 1.38
<1b>[1m RMSE<1b>[22m: 1.18
<1b>[1m R²<1b>[22m: 0.69
2026-10-05 20:12:32 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 1.56 seconds.<1b>[0m [train]
2026-10-05 20:12:32 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:32 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:12:32 <1b>[0mTraining set: 400 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:32 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:12:32 <1b>[0m<> Training Ranger Regression using 3 independent folds...<1b>[0m [train]
2026-10-05 20:12:32 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
\ 1/3 ETA: 5s | Training outer resamples...
2026-10-05 20:12:40 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Regression Model>
Ranger (Random Forest)
⟳ Tested using 3 independent folds.
<Resampled Regression Training Metrics>
Showing mean (sd) across resamples.
MAE: 0.412 (3.1e-03)
MSE: 0.264 (4.9e-03)
RMSE: 0.514 (4.8e-03)
R²: 0.946 (1.7e-03)
<Resampled Regression Test Metrics>
Showing mean (sd) across resamples.
MAE: 0.878 (3.4e-03)
MSE: 1.188 (0.009)
RMSE: 1.090 (4e-03)
R²: 0.758 (0.007)
2026-10-05 20:12:40 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 8.12 seconds.<1b>[0m [train]
| 2/3 ETA: 4s | Training outer resamples...
2026-10-05 20:12:40 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:40 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:12:40 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:40 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:40 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:12:40 <1b>[0mTraining Ranger Classification...<1b>[0m [train]
2026-10-05 20:12:40 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mRanger<1b>[0m (Random Forest)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 44 1
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 2 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.967
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.957
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.977
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.967
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.967
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.998
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.024
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.071
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 20:12:40 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.12 seconds.<1b>[0m [train]
2026-10-05 20:12:40 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:40 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:12:40 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:40 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:40 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:12:40 <1b>[0m<> Tuning Ranger by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 20:12:40 <1b>[0m2 parameter combinations x 5 resamples: 10 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 20:12:40 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:12:40 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 20:12:40 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
2026-10-05 20:12:41 <1b>[0mBest config to maximize balanced_accuracy:<1b>[0m [tune_GridSearch]
mtry: {2, 4} => 4
2026-10-05 20:12:41 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 20:12:41 <1b>[0mTraining Ranger Classification with tuned hyperparameters...<1b>[0m [train]
2026-10-05 20:12:41 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mRanger<1b>[0m (Random Forest)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 43 2
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 2 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.956
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.997
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.025
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.051
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 20:12:41 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 1.07 seconds.<1b>[0m [train]
2026-10-05 20:12:41 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:41 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:12:41 <1b>[0mTraining set: 100 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:41 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:12:41 <1b>[0m<> Training Ranger Classification using 3 independent folds...<1b>[0m [train]
2026-10-05 20:12:41 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:12:41 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 20:12:41 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Classification Model>
Ranger (Random Forest)
⟳ Tested using 3 independent folds.
<Resampled Classification Training Metrics>
Aggregate Confusion Matrix across resamples.
Predicted
Reference virginica versicolor
virginica 97 3
versicolor 3 97
Showing mean (sd) across resamples.
Sensitivity: 0.970 (0.029)
Specificity: 0.970 (0.029)
Balanced Accuracy: 0.970 (0.025)
Ppv: 0.971 (0.029)
Npv: 0.971 (0.029)
F1: 0.970 (0.025)
Accuracy: 0.970 (0.025)
Auc: 0.997 (2.7e-03)
Brier Score: 0.021 (0.011)
<Resampled Classification Test Metrics>
Aggregate Confusion Matrix across resamples.
Predicted
Reference virginica versicolor
virginica 46 4
versicolor 3 47
Showing mean (sd) across resamples.
Sensitivity: 0.920 (0.089)
Specificity: 0.940 (2.1e-03)
Balanced Accuracy: 0.930 (0.045)
Ppv: 0.938 (0.006)
Npv: 0.927 (0.080)
F1: 0.928 (0.049)
Accuracy: 0.930 (0.045)
Auc: 0.984 (9.8e-04)
Brier Score: 0.065 (0.027)
2026-10-05 20:12:41 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.31 seconds.<1b>[0m [train]
2026-10-05 20:12:41 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:41 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:12:41 <1b>[0mTraining set: 135 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:41 <1b>[0m Test set: 15 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:41 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:12:41 <1b>[0mTraining Ranger Classification...<1b>[0m [train]
2026-10-05 20:12:41 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mRanger<1b>[0m (Random Forest)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 45 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 42 3
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 2 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.963
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.963
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.963
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 0.933 0.956
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 0.978 0.967
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000 0.956 0.961
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 0.955 0.935
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 0.967 0.978
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000 0.944 0.945
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 5 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 5 0
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 0 5
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 1.000
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000 1.000 1.000
2026-10-05 20:12:42 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.12 seconds.<1b>[0m [train]
2026-10-05 20:12:42 <1b>[0m<> Calibrating LightRF classification...<1b>[0m [calibrate]
2026-10-05 20:12:42 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:42 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:12:42 <1b>[0mTraining set: 90 cases x 1 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:42 <1b>[0m Test set: 10 cases x 1 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:42 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:12:42 <1b>[0mTraining Isotonic Classification...<1b>[0m [train]
2026-10-05 20:12:42 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mIsotonic<1b>[0m (Isotonic Regression)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 45 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 2 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.957
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.997
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.017
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.100
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 20:12:42 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.06 seconds.<1b>[0m [train]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightRF<1b>[0m (LightGBM Random Forest)
<1b>[1;38;2;115;100;242m⟋<1b>[0m Calibrated using Isotonic Regression.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics (Pre => Post Calibration)<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 41 => 45 4 => 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 => 2 44 => 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.91 => 1.00
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.98 => 0.96
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.94 => 0.98
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.98 => 0.96
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.92 => 1.00
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.94 => 0.98
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.94 => 0.98
<1b>[22;38;2;128;128;128m Auc<1b>[0m 1.00 => 1.00
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.05 => 0.02
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics (Pre => Post Calibration)<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 => 5 0 => 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 => 1 4 => 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.00 => 1.00
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.80 => 0.80
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.90 => 0.90
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.83 => 0.83
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.00 => 1.00
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.91 => 0.91
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.90 => 0.90
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.90 => 0.90
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.09 => 0.10
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 20:12:42 <1b>[0m</> Calibration done.<1b>[0m [calibrate]
2026-10-05 20:12:42 <1b>[0mConverting 1 factor to integer...<1b>[0m [preprocess]
2026-10-05 20:12:42 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
2026-10-05 20:12:42 <1b>[0m<> Calibrating LightRF resampled classification...<1b>[0m [calibrate]
<Resampled Classification Model>
LightRF (LightGBM Random Forest)
⟳ Tested using 3 independent folds.
⟋ Calibrated using Isotonic Regression with 5 independent folds.
<Resampled Classification Training Metrics (Pre => Post Calibration)>
Showing mean (sd) across resamples, Pre => Post calibration.
sensitivity: 0.95 (0.02) => 1.00 (0.00)
specificity: 0.96 (0.04) => 0.92 (0.08)
balanced_accuracy: 0.96 (0.01) => 0.96 (0.04)
ppv: 0.96 (0.04) => 0.93 (0.07)
npv: 0.95 (0.02) => 1.00 (0.00)
f1: 0.96 (0.01) => 0.96 (0.04)
accuracy: 0.96 (0.01) => 0.96 (0.04)
auc: 0.99 (0.01) => 0.98 (0.02)
brier_score: 0.11 (0.02) => 0.03 (0.03)
<Resampled Classification Test Metrics (Pre => Post Calibration)>
Showing mean (sd) across resamples, Pre => Post calibration.
sensitivity: 0.90 (0.07) => 0.93 (0.19)
specificity: 0.94 (0.10) => 0.85 (0.20)
balanced_accuracy: 0.92 (0.04) => 0.89 (0.12)
ppv: 0.95 (0.09) => 0.89 (0.14)
npv: 0.91 (0.05) => 0.96 (0.12)
f1: 0.92 (0.04) => 0.89 (0.14)
accuracy: 0.92 (0.04) => 0.89 (0.12)
auc: 0.98 (0.03) => 0.92 (0.11)
brier_score: 0.12 (0.04) => 0.09 (0.11)
2026-10-05 20:12:43 <1b>[0m</> Calibration done.<1b>[0m [calibrate]
Generalized Linear Model was used for regression. R-squared was 0.83 on the training set and 0.77 on the test set.
Generalized Linear Model was used for regression. R-squared was 0.83 on the training set and 0.77 on the test set.
Generalized Linear Model was used for classification. Balanced accuracy was 0.98 in the training set and 0.90 in the test set.
Generalized Linear Model was used for regression. R-squared was 0.83 on the training set and 0.77 on the test set.
Generalized Linear Model was used for classification. Balanced accuracy was 0.98 in the training set and 0.90 in the test set.
Generalized Linear Model was used for regression. Mean R-squared was 0.83 on the training set and 0.82 on the test set across 3 independent folds.
Generalized Linear Model was used for classification. Mean balanced accuracy was 0.99 in the training set and 0.89 in the test set across 3 independent folds.
Generalized Linear Model (GLM) and Classification and Regression Trees (CART) were used for Regression.
The top-performing model was GLM with a test-set Rsq of 0.817, followed by CART with rsq of 0.629 respectively.
Generalized Linear Model (GLM) and Classification and Regression Trees (CART) were used for Classification.
The top-performing model was CART with a test-set Balanced Accuracy of 0.920, followed by GLM with balanced_accuracy of 0.888 respectively.
Generalized Linear Model (GLM) and Classification and Regression Trees (CART) were used for Regression.
The top-performing model was GLM with a test-set Rsq of 0.770, followed by CART with rsq of 0.595 respectively.
2026-10-05 20:12:45 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:45 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:12:45 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:45 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:45 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:12:45 <1b>[0mTraining GLM Regression...<1b>[0m [train]
2026-10-05 20:12:45 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLM<1b>[0m (Generalized Linear Model)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.73
<1b>[1m MSE<1b>[22m: 0.82
<1b>[1m RMSE<1b>[22m: 0.91
<1b>[1m R²<1b>[22m: 0.83
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.74
<1b>[1m MSE<1b>[22m: 1.03
<1b>[1m RMSE<1b>[22m: 1.01
<1b>[1m R²<1b>[22m: 0.77
2026-10-05 20:12:45 <1b>[0mWriting data to /home/hornik/tmp/scratch/RtmpoTlsEM/file1fcc721aa01c38/mod_r_glm...✔ 0.14 secs [rt_save]
2026-10-05 20:12:46 <1b>[0mReload with: > obj <- readRDS('/home/hornik/tmp/scratch/RtmpoTlsEM/file1fcc721aa01c38/mod_r_glm/train_GLM.rds')<1b>[0m [rt_save]
2026-10-05 20:12:46 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.20 seconds.<1b>[0m [train]
2026-10-05 20:12:46 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:46 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:12:46 <1b>[0mTraining set: 400 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:46 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: c(`_R_CHECK_LIMIT_CORES_` = 1) }<1b>[0m [get_n_workers]
2026-10-05 20:12:46 <1b>[0m<> Training GLM Regression using 3 independent folds...<1b>[0m [train]
2026-10-05 20:12:46 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:12:46 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Regression Model>
GLM (Generalized Linear Model)
⟳ Tested using 3 independent folds.
<Resampled Regression Training Metrics>
Showing mean (sd) across resamples.
MAE: 0.723 (0.039)
MSE: 0.831 (0.080)
RMSE: 0.911 (0.044)
R²: 0.830 (0.019)
<Resampled Regression Test Metrics>
Showing mean (sd) across resamples.
MAE: 0.756 (0.096)
MSE: 0.909 (0.188)
RMSE: 0.950 (0.101)
R²: 0.813 (0.046)
2026-10-05 20:12:46 <1b>[0mWriting data to /home/hornik/tmp/scratch/RtmpoTlsEM/file1fcc727904b901/resmod_r_glm...✔ 0.62 secs [rt_save]
2026-10-05 20:12:46 <1b>[0mReload with: > obj <- readRDS('/home/hornik/tmp/scratch/RtmpoTlsEM/file1fcc727904b901/resmod_r_glm/train_GLM.rds')<1b>[0m [rt_save]
2026-10-05 20:12:46 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.84 seconds.<1b>[0m [train]
Classification and Regression Trees (CART), LightGBM Random Forest (LightRF), and Gradient Boosting (LightGBM) were used for Classification.
The top-performing model was CART with a test-set Balanced Accuracy of 0.900, followed by LightRF and LightGBM with balanced_accuracy of 0.900 and 0.900 respectively.
Classification and Regression Trees (CART), LightGBM Random Forest (LightRF), and Gradient Boosting (LightGBM) were used for Classification.
The top-performing model was CART with a test-set Balanced Accuracy of 0.900, followed by LightRF and LightGBM with balanced_accuracy of 0.900 and 0.900 respectively.
Elastic Net (GLMNET), Support Vector Machine with Radial Kernel (RadialSVM), and LightGBM Random Forest (LightRF) were used for Regression.
The top-performing model was RadialSVM with a test-set Rsq of 0.773, followed by GLMNET and LightRF with rsq of 0.772 and 0.735 respectively.
Elastic Net (GLMNET), Support Vector Machine with Radial Kernel (RadialSVM), and LightGBM Random Forest (LightRF) were used for Regression.
The top-performing model was RadialSVM with a test-set Rsq of 0.773, followed by GLMNET and LightRF with rsq of 0.772 and 0.735 respectively.
Generalized Linear Model (GLM), Support Vector Machine with Linear Kernel (LinearSVM), and LightGBM Random Forest (LightRF) were used for Classification.
The top-performing model was LinearSVM with a test-set Balanced Accuracy of 0.960, followed by LightRF and GLM with balanced_accuracy of 0.921 and 0.888 respectively.
Generalized Linear Model (GLM), Support Vector Machine with Linear Kernel (LinearSVM), and LightGBM Random Forest (LightRF) were used for Classification.
The top-performing model was LinearSVM with a test-set Balanced Accuracy of 0.960, followed by LightRF and GLM with balanced_accuracy of 0.921 and 0.888 respectively.
Generalized Linear Model (GLM), Support Vector Machine with Linear Kernel (LinearSVM), and LightGBM Random Forest (LightRF) were used for Regression.
The top-performing model was LinearSVM with a test-set Rsq of 0.821, followed by GLM and LightRF with rsq of 0.817 and 0.661 respectively.
Generalized Linear Model (GLM), Support Vector Machine with Linear Kernel (LinearSVM), and LightGBM Random Forest (LightRF) were used for Regression.
The top-performing model was LinearSVM with a test-set Rsq of 0.821, followed by GLM and LightRF with rsq of 0.817 and 0.661 respectively.
2026-10-05 20:12:47 <1b>[0m<> Calibrating GLM resampled classification...<1b>[0m [calibrate]
<Resampled Classification Model>
GLM (Generalized Linear Model)
⟳ Tested using 3 independent folds.
⟋ Calibrated using Isotonic Regression with 5 independent folds.
<Resampled Classification Training Metrics (Pre => Post Calibration)>
Showing mean (sd) across resamples, Pre => Post calibration.
sensitivity: 0.99 (0.02) => 0.95 (0.08)
specificity: 0.99 (0.02) => 0.91 (0.09)
balanced_accuracy: 0.99 (0.02) => 0.93 (0.07)
ppv: 0.99 (0.02) => 0.92 (0.08)
npv: 0.99 (0.02) => 0.95 (0.07)
f1: 0.99 (0.02) => 0.93 (0.07)
accuracy: 0.99 (0.02) => 0.93 (0.07)
auc: 1.00 (3.7e-03) => 0.94 (0.05)
brier_score: 0.01 (0.01) => 0.05 (0.05)
<Resampled Classification Test Metrics (Pre => Post Calibration)>
Showing mean (sd) across resamples, Pre => Post calibration.
sensitivity: 0.88 (0.13) => 0.94 (0.13)
specificity: 0.90 (0.09) => 0.89 (0.20)
balanced_accuracy: 0.89 (0.09) => 0.91 (0.12)
ppv: 0.90 (0.10) => 0.92 (0.15)
npv: 0.89 (0.12) => 0.94 (0.12)
f1: 0.89 (0.10) => 0.92 (0.11)
accuracy: 0.89 (0.09) => 0.91 (0.12)
auc: 0.94 (0.06) => 0.92 (0.12)
brier_score: 0.10 (0.09) => 0.08 (0.11)
2026-10-05 20:12:48 <1b>[0m</> Calibration done.<1b>[0m [calibrate]
2026-10-05 20:12:48 <1b>[0m<> Calibrating CART resampled classification...<1b>[0m [calibrate]
<Resampled Classification Model>
CART (Classification and Regression Trees)
⚙ Tuned using exhaustive grid search.
⟳ Tested using 3 independent folds.
⟋ Calibrated using Isotonic Regression with 5 independent folds.
<Resampled Classification Training Metrics (Pre => Post Calibration)>
Showing mean (sd) across resamples, Pre => Post calibration.
sensitivity: 0.94 (0.06) => 0.92 (0.08)
specificity: 0.95 (0.06) => 0.92 (0.08)
balanced_accuracy: 0.95 (0.01) => 0.92 (0.02)
ppv: 0.95 (0.06) => 0.93 (0.07)
npv: 0.95 (0.05) => 0.93 (0.07)
f1: 0.94 (0.01) => 0.92 (0.02)
accuracy: 0.95 (0.01) => 0.92 (0.02)
auc: 0.95 (0.01) => 0.92 (0.02)
brier_score: 0.05 (0.01) => 0.07 (0.02)
<Resampled Classification Test Metrics (Pre => Post Calibration)>
Showing mean (sd) across resamples, Pre => Post calibration.
sensitivity: 0.92 (0.09) => 0.92 (0.14)
specificity: 0.92 (0.09) => 0.92 (0.14)
balanced_accuracy: 0.92 (0.01) => 0.92 (0.08)
ppv: 0.93 (0.08) => 0.94 (0.10)
npv: 0.93 (0.08) => 0.94 (0.11)
f1: 0.92 (0.02) => 0.92 (0.09)
accuracy: 0.92 (0.01) => 0.92 (0.08)
auc: 0.91 (0.03) => 0.92 (0.08)
brier_score: 0.07 (0.01) => 0.08 (0.06)
2026-10-05 20:12:49 <1b>[0m</> Calibration done.<1b>[0m [calibrate]
2026-10-05 20:12:49 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:49 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:12:49 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:49 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:49 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:12:49 <1b>[0mPreprocessing...<1b>[0m [train]
2026-10-05 20:12:49 <1b>[0mTraining GLM Classification...<1b>[0m [train]
2026-10-05 20:12:49 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLM<1b>[0m (Generalized Linear Model)
<1b>[1;38;2;82;101;81m▣<1b>[0m Preprocessed using centering, scaling.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 44 1
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 44
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.978
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.998
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.018
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.093
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 20:12:49 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.06 seconds.<1b>[0m [train]
rtemis Color System
<1b>[1m highlight_col<1b>[22m: <1b>[22;38;2;108;163;160m░<1b>[0m<1b>[22;38;2;108;163;160m░<1b>[0m<1b>[22;38;2;108;163;160m▓<1b>[0m<1b>[22;38;2;108;163;160m▓<1b>[0m<1b>[22;38;2;108;163;160m█<1b>[0m<1b>[22;38;2;108;163;160m█<1b>[0m<1b>[22;38;2;108;163;160m▓<1b>[0m<1b>[22;38;2;108;163;160m▓<1b>[0m<1b>[22;38;2;108;163;160m░<1b>[0m<1b>[22;38;2;108;163;160m░<1b>[0m
<1b>[1m col_warn<1b>[22m: <1b>[22;38;2;240;137;4m░<1b>[0m<1b>[22;38;2;240;137;4m░<1b>[0m<1b>[22;38;2;240;137;4m▓<1b>[0m<1b>[22;38;2;240;137;4m▓<1b>[0m<1b>[22;38;2;240;137;4m█<1b>[0m<1b>[22;38;2;240;137;4m█<1b>[0m<1b>[22;38;2;240;137;4m▓<1b>[0m<1b>[22;38;2;240;137;4m▓<1b>[0m<1b>[22;38;2;240;137;4m░<1b>[0m<1b>[22;38;2;240;137;4m░<1b>[0m
<1b>[1m col_error<1b>[22m: <1b>[22;38;2;234;56;74m░<1b>[0m<1b>[22;38;2;234;56;74m░<1b>[0m<1b>[22;38;2;234;56;74m▓<1b>[0m<1b>[22;38;2;234;56;74m▓<1b>[0m<1b>[22;38;2;234;56;74m█<1b>[0m<1b>[22;38;2;234;56;74m█<1b>[0m<1b>[22;38;2;234;56;74m▓<1b>[0m<1b>[22;38;2;234;56;74m▓<1b>[0m<1b>[22;38;2;234;56;74m░<1b>[0m<1b>[22;38;2;234;56;74m░<1b>[0m
<1b>[1m col_success<1b>[22m: <1b>[22;38;2;0;204;143m░<1b>[0m<1b>[22;38;2;0;204;143m░<1b>[0m<1b>[22;38;2;0;204;143m▓<1b>[0m<1b>[22;38;2;0;204;143m▓<1b>[0m<1b>[22;38;2;0;204;143m█<1b>[0m<1b>[22;38;2;0;204;143m█<1b>[0m<1b>[22;38;2;0;204;143m▓<1b>[0m<1b>[22;38;2;0;204;143m▓<1b>[0m<1b>[22;38;2;0;204;143m░<1b>[0m<1b>[22;38;2;0;204;143m░<1b>[0m
<1b>[1m col_preprocessor<1b>[22m: <1b>[22;38;2;82;101;81m░<1b>[0m<1b>[22;38;2;82;101;81m░<1b>[0m<1b>[22;38;2;82;101;81m▓<1b>[0m<1b>[22;38;2;82;101;81m▓<1b>[0m<1b>[22;38;2;82;101;81m█<1b>[0m<1b>[22;38;2;82;101;81m█<1b>[0m<1b>[22;38;2;82;101;81m▓<1b>[0m<1b>[22;38;2;82;101;81m▓<1b>[0m<1b>[22;38;2;82;101;81m░<1b>[0m<1b>[22;38;2;82;101;81m░<1b>[0m
<1b>[1m col_decom<1b>[22m: <1b>[22;38;2;15;106;102m░<1b>[0m<1b>[22;38;2;15;106;102m░<1b>[0m<1b>[22;38;2;15;106;102m▓<1b>[0m<1b>[22;38;2;15;106;102m▓<1b>[0m<1b>[22;38;2;15;106;102m█<1b>[0m<1b>[22;38;2;15;106;102m█<1b>[0m<1b>[22;38;2;15;106;102m▓<1b>[0m<1b>[22;38;2;15;106;102m▓<1b>[0m<1b>[22;38;2;15;106;102m░<1b>[0m<1b>[22;38;2;15;106;102m░<1b>[0m
<1b>[1m col_outer<1b>[22m: <1b>[22;38;2;190;46;95m░<1b>[0m<1b>[22;38;2;190;46;95m░<1b>[0m<1b>[22;38;2;190;46;95m▓<1b>[0m<1b>[22;38;2;190;46;95m▓<1b>[0m<1b>[22;38;2;190;46;95m█<1b>[0m<1b>[22;38;2;190;46;95m█<1b>[0m<1b>[22;38;2;190;46;95m▓<1b>[0m<1b>[22;38;2;190;46;95m▓<1b>[0m<1b>[22;38;2;190;46;95m░<1b>[0m<1b>[22;38;2;190;46;95m░<1b>[0m
<1b>[1m col_tuner<1b>[22m: <1b>[22;38;2;243;132;255m░<1b>[0m<1b>[22;38;2;243;132;255m░<1b>[0m<1b>[22;38;2;243;132;255m▓<1b>[0m<1b>[22;38;2;243;132;255m▓<1b>[0m<1b>[22;38;2;243;132;255m█<1b>[0m<1b>[22;38;2;243;132;255m█<1b>[0m<1b>[22;38;2;243;132;255m▓<1b>[0m<1b>[22;38;2;243;132;255m▓<1b>[0m<1b>[22;38;2;243;132;255m░<1b>[0m<1b>[22;38;2;243;132;255m░<1b>[0m
<1b>[1m col_info<1b>[22m: <1b>[22;38;2;70;109;150m░<1b>[0m<1b>[22;38;2;70;109;150m░<1b>[0m<1b>[22;38;2;70;109;150m▓<1b>[0m<1b>[22;38;2;70;109;150m▓<1b>[0m<1b>[22;38;2;70;109;150m█<1b>[0m<1b>[22;38;2;70;109;150m█<1b>[0m<1b>[22;38;2;70;109;150m▓<1b>[0m<1b>[22;38;2;70;109;150m▓<1b>[0m<1b>[22;38;2;70;109;150m░<1b>[0m<1b>[22;38;2;70;109;150m░<1b>[0m
Group counts:
group
Low NS High
1 98 1
2026-10-05 20:12:58 <1b>[0m▶<1b>[0m [massGLM]
2026-10-05 20:12:58 <1b>[0mScaling and centering 40 numeric features...<1b>[0m [preprocess]
2026-10-05 20:12:58 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
2026-10-05 20:12:58 <1b>[0mFitting 40 GLMs of family gaussian with 2 predictors each...<1b>[0m [massGLM]
2026-10-05 20:12:58 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.11 seconds.<1b>[0m [massGLM]
2026-10-05 20:12:58 <1b>[0mPlotting coefficients for x1 x 40 outcomes.<1b>[0m [`plot.rtemis::MassGLM`]
Group counts:
group
Low NS High
1 37 2
2026-10-05 20:12:58 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:58 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:12:58 <1b>[0mTraining set: 100 cases x 3 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:58 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:12:58 <1b>[0mTraining GLM Regression...<1b>[0m [train]
2026-10-05 20:12:58 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLM<1b>[0m (Generalized Linear Model)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.84
<1b>[1m MSE<1b>[22m: 1.04
<1b>[1m RMSE<1b>[22m: 1.02
<1b>[1m R²<1b>[22m: 0.69
2026-10-05 20:12:58 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.04 seconds.<1b>[0m [train]
2026-10-05 20:12:58 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:58 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:12:58 <1b>[0mTraining set: 100 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:58 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 20:12:58 <1b>[0mTraining GLM Classification...<1b>[0m [train]
2026-10-05 20:12:58 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLM<1b>[0m (Generalized Linear Model)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 49 1
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 49
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.980
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.980
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.980
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.980
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.980
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.980
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.980
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.997
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.019
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 20:12:58 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.05 seconds.<1b>[0m [train]
2026-10-05 20:12:58 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 20:12:58 <1b>[0m▶<1b>[0m [train]
2026-10-05 20:12:58 <1b>[0mTraining set: 100 cases x 3 features.<1b>[0m [summarize_supervised]
2026-10-05 20:12:58 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: c(`_R_CHECK_LIMIT_CORES_` = 1) }<1b>[0m [get_n_workers]
2026-10-05 20:12:58 <1b>[0m<> Training GLM Regression using 3 independent folds...<1b>[0m [train]
2026-10-05 20:12:58 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 20:12:59 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Regression Model>
GLM (Generalized Linear Model)
⟳ Tested using 3 independent folds.
<Resampled Regression Training Metrics>
Showing mean (sd) across resamples.
MAE: 0.821 (0.012)
MSE: 1.008 (0.053)
RMSE: 1.004 (0.026)
R²: 0.697 (0.020)
<Resampled Regression Test Metrics>
Showing mean (sd) across resamples.
MAE: 0.901 (0.029)
MSE: 1.193 (0.074)
RMSE: 1.092 (0.034)
R²: 0.637 (0.047)
2026-10-05 20:12:59 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.20 seconds.<1b>[0m [train]
[ FAIL 1 | WARN 4 | SKIP 4 | PASS 356 ]
══ Skipped tests (4) ═══════════════════════════════════════════════════════════
• For local testing only; requires CSV file (3): 'test_ClusterConfig.R:19:3',
'test_DecomposeConfig.R:19:3', 'test_SuperConfig.R:48:3'
• empty test (1):
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test_Clustering.R:96:3'): cluster_DBSCAN() succeeds ─────────────────
Error: approx must be a single, finite, nonnegative number.
Backtrace:
▆
1. └─rtemis::cluster(...) at test_Clustering.R:96:3
2. └─rtemis:::cluster_(config = config, x = x, verbosity = verbosity)
3. ├─S7::S7_dispatch()
4. └─rtemis (local) `method(cluster_, rtemis::DBSCANConfig)`(...)
5. └─dbscan::dbscan(...)
6. └─dbscan:::.validate_nonnegative_scalar(extra$approx %||% 0, "approx")
[ FAIL 1 | WARN 4 | SKIP 4 | PASS 356 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 1.2.7
Check: tests
Result: ERROR
Running ‘testthat.R’ [84s/216s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(rtemis)
.:rtemis 1.2.7 🌊 x86_64-pc-linux-gnu
> library(testthat)
Attaching package: 'testthat'
The following object is masked from 'package:rtemis':
describe
>
> test_check("rtemis")
Attaching package: 'data.table'
The following object is masked from 'package:base':
%notin%
2026-10-05 11:22:02 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:22:02 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:22:02 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 11:22:02 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:22:02 <1b>[0mUsing max n bins possible = 3.<1b>[0m [kfold]
2026-10-05 11:22:03 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Created file: /tmp/RtmpNpyEX1/working_dir/RtmpjfjDos/rtemis_cluster.json<1b>[0m [write_lines]
2026-10-05 11:22:03 ✖ rtemis_range_error<1b>[0m [setup_KMeans]
2026-10-05 11:22:03 <1b>[0m▶<1b>[0m [cluster]
2026-10-05 11:22:03 <1b>[0mInput: 150 cases x 4 features.<1b>[0m [summarize_unsupervised]
2026-10-05 11:22:03 <1b>[0mClustering with KMeans...<1b>[0m [cluster]
2026-10-05 11:22:03 <1b>[0mChecking unsupervised data... ✔ [check_unsupervised_data]
2026-10-05 11:22:03 <1b>[0mClustering with KMeans ...<1b>[0m [cluster_]
2026-10-05 11:22:03 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.54 seconds.<1b>[0m [cluster]
2026-10-05 11:22:03 <1b>[0m▶<1b>[0m [cluster]
2026-10-05 11:22:03 <1b>[0mInput: 150 cases x 4 features.<1b>[0m [summarize_unsupervised]
2026-10-05 11:22:03 <1b>[0mClustering with KMeans...<1b>[0m [cluster]
2026-10-05 11:22:03 <1b>[0mChecking unsupervised data... ✔ [check_unsupervised_data]
2026-10-05 11:22:03 <1b>[0mClustering with KMeans ...<1b>[0m [cluster_]
2026-10-05 11:22:04 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.32 seconds.<1b>[0m [cluster]
2026-10-05 11:22:04 <1b>[0m▶<1b>[0m [cluster]
2026-10-05 11:22:04 <1b>[0mInput: 150 cases x 4 features.<1b>[0m [summarize_unsupervised]
2026-10-05 11:22:04 <1b>[0mClustering with HardCL...<1b>[0m [cluster]
2026-10-05 11:22:04 <1b>[0mChecking unsupervised data... ✔ [check_unsupervised_data]
2026-10-05 11:22:04 <1b>[0mClustering with HardCL ...<1b>[0m [cluster_]
2026-10-05 11:22:04 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.10 seconds.<1b>[0m [cluster]
2026-10-05 11:22:04 <1b>[0m▶<1b>[0m [cluster]
2026-10-05 11:22:04 <1b>[0mInput: 150 cases x 4 features.<1b>[0m [summarize_unsupervised]
2026-10-05 11:22:04 <1b>[0mClustering with NeuralGas...<1b>[0m [cluster]
2026-10-05 11:22:04 <1b>[0mChecking unsupervised data... ✔ [check_unsupervised_data]
2026-10-05 11:22:04 <1b>[0mClustering with NeuralGas ...<1b>[0m [cluster_]
2026-10-05 11:22:05 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.11 seconds.<1b>[0m [cluster]
2026-10-05 11:22:05 <1b>[0m▶<1b>[0m [cluster]
2026-10-05 11:22:05 <1b>[0mInput: 150 cases x 4 features.<1b>[0m [summarize_unsupervised]
2026-10-05 11:22:05 <1b>[0mClustering with CMeans...<1b>[0m [cluster]
2026-10-05 11:22:05 <1b>[0mChecking unsupervised data... ✔ [check_unsupervised_data]
2026-10-05 11:22:05 <1b>[0mClustering with CMeans ...<1b>[0m [cluster_]
Iteration: 1, Error: 1.0222276897
Iteration: 2, Error: 0.4426354825
Iteration: 3, Error: 0.4040115623
Iteration: 4, Error: 0.4035611242
Iteration: 5, Error: 0.4034484182
Iteration: 6, Error: 0.4034031102
Iteration: 7, Error: 0.4033845090
Iteration: 8, Error: 0.4033768332
Iteration: 9, Error: 0.4033736566
Iteration: 10, Error: 0.4033723396
Iteration: 11, Error: 0.4033717929
Iteration: 12, Error: 0.4033715658
Iteration: 13, Error: 0.4033714714
Iteration: 14, Error: 0.4033714321
Iteration: 15, Error: 0.4033714158
Iteration: 16, Error: 0.4033714090
Iteration: 17 converged, Error: 0.4033714062
2026-10-05 11:22:05 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.03 seconds.<1b>[0m [cluster]
2026-10-05 11:22:05 <1b>[0m▶<1b>[0m [cluster]
2026-10-05 11:22:05 <1b>[0mInput: 150 cases x 4 features.<1b>[0m [summarize_unsupervised]
2026-10-05 11:22:05 <1b>[0mClustering with DBSCAN...<1b>[0m [cluster]
2026-10-05 11:22:05 <1b>[0mChecking unsupervised data... ✔ [check_unsupervised_data]
2026-10-05 11:22:05 <1b>[0mClustering with DBSCAN ...<1b>[0m [cluster_]
Saving _problems/test_Clustering-100.R
2026-10-05 11:22:06 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Created file: /tmp/RtmpNpyEX1/working_dir/RtmpjfjDos/rtemis_decompose.json<1b>[0m [write_lines]
2026-10-05 11:22:06 <1b>[0m▶<1b>[0m [decomp]
2026-10-05 11:22:06 <1b>[0mInput: 150 cases x 4 features.<1b>[0m [summarize_unsupervised]
2026-10-05 11:22:06 <1b>[0mDecomposing with PCA...<1b>[0m [decomp]
2026-10-05 11:22:06 <1b>[0mChecking unsupervised data... ✔ [check_unsupervised_data]
2026-10-05 11:22:06 <1b>[0mDecomposing with PCA ...<1b>[0m [decomp_]
2026-10-05 11:22:06 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.03 seconds.<1b>[0m [decomp]
2026-10-05 11:22:06 <1b>[0m▶<1b>[0m [decomp]
2026-10-05 11:22:06 <1b>[0mInput: 150 cases x 4 features.<1b>[0m [summarize_unsupervised]
2026-10-05 11:22:06 <1b>[0mDecomposing with ICA...<1b>[0m [decomp]
2026-10-05 11:22:06 <1b>[0mChecking unsupervised data... ✔ [check_unsupervised_data]
2026-10-05 11:22:06 <1b>[0mDecomposing with ICA ...<1b>[0m [decomp_]
Centering
colstandard
Whitening
Symmetric FastICA using logcosh approx. to neg-entropy function
Iteration 1 tol=0.038537
Iteration 2 tol=0.004207
Iteration 3 tol=0.002599
Iteration 4 tol=0.001640
Iteration 5 tol=0.000951
Iteration 6 tol=0.000483
Iteration 7 tol=0.000217
Iteration 8 tol=0.000088
2026-10-05 11:22:06 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.02 seconds.<1b>[0m [decomp]
2026-10-05 11:22:10 <1b>[0m▶<1b>[0m [decomp]
2026-10-05 11:22:10 <1b>[0mInput: 150 cases x 4 features.<1b>[0m [summarize_unsupervised]
2026-10-05 11:22:10 <1b>[0mDecomposing with NMF...<1b>[0m [decomp]
2026-10-05 11:22:10 <1b>[0mChecking unsupervised data... ✔ [check_unsupervised_data]
2026-10-05 11:22:10 <1b>[0mDecomposing with NMF ...<1b>[0m [decomp_]
2026-10-05 11:22:17 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 6.72 seconds.<1b>[0m [decomp]
2026-10-05 11:22:17 <1b>[0m▶<1b>[0m [decomp]
2026-10-05 11:22:17 <1b>[0mInput: 150 cases x 4 features.<1b>[0m [summarize_unsupervised]
2026-10-05 11:22:17 <1b>[0mDecomposing with UMAP...<1b>[0m [decomp]
2026-10-05 11:22:17 <1b>[0mChecking unsupervised data... ✔ [check_unsupervised_data]
2026-10-05 11:22:17 <1b>[0mDecomposing with UMAP ...<1b>[0m [decomp_]
2026-10-05 11:22:21 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 4.51 seconds.<1b>[0m [decomp]
2026-10-05 11:22:21 <1b>[0m▶<1b>[0m [decomp]
2026-10-05 11:22:21 <1b>[0mInput: 150 cases x 4 features.<1b>[0m [summarize_unsupervised]
2026-10-05 11:22:21 <1b>[0mDecomposing with UMAP...<1b>[0m [decomp]
2026-10-05 11:22:21 <1b>[0mChecking unsupervised data... ✔ [check_unsupervised_data]
2026-10-05 11:22:22 <1b>[0mDecomposing with UMAP ...<1b>[0m [decomp_]
2026-10-05 11:22:25 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 3.51 seconds.<1b>[0m [decomp]
2026-10-05 11:22:25 <1b>[0m▶<1b>[0m [decomp]
2026-10-05 11:22:25 <1b>[0mInput: 150 cases x 4 features.<1b>[0m [summarize_unsupervised]
2026-10-05 11:22:25 <1b>[0mDecomposing with tSNE...<1b>[0m [decomp]
2026-10-05 11:22:25 <1b>[0mChecking unsupervised data... ✔ [check_unsupervised_data]
2026-10-05 11:22:25 <1b>[0mDecomposing with tSNE ...<1b>[0m [decomp_]
2026-10-05 11:22:25 <1b>[0mRemoving 1 duplicate case...<1b>[0m [preprocess]
2026-10-05 11:22:25 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
2026-10-05 11:22:25 <1b>[0m▶<1b>[0m [decomp]
2026-10-05 11:22:25 <1b>[0mInput: 149 cases x 4 features.<1b>[0m [summarize_unsupervised]
2026-10-05 11:22:25 <1b>[0mDecomposing with tSNE...<1b>[0m [decomp]
2026-10-05 11:22:25 <1b>[0mChecking unsupervised data... ✔ [check_unsupervised_data]
2026-10-05 11:22:25 <1b>[0mDecomposing with tSNE ...<1b>[0m [decomp_]
2026-10-05 11:22:26 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.75 seconds.<1b>[0m [decomp]
2026-10-05 11:22:26 <1b>[0m▶<1b>[0m [decomp]
2026-10-05 11:22:26 <1b>[0mInput: 150 cases x 4 features.<1b>[0m [summarize_unsupervised]
2026-10-05 11:22:26 <1b>[0mDecomposing with Isomap...<1b>[0m [decomp]
2026-10-05 11:22:26 <1b>[0mChecking unsupervised data... ✔ [check_unsupervised_data]
2026-10-05 11:22:26 <1b>[0mDecomposing with Isomap ...<1b>[0m [decomp_]
2026-10-05 11:22:26 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.09 seconds.<1b>[0m [decomp]
2026-10-05 11:22:27 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:22:27 <1b>[0mUsing max n bins possible = 3.<1b>[0m [kfold]
2026-10-05 11:22:28 <1b>[0mScaling and centering 4 numeric features...<1b>[0m [preprocess]
2026-10-05 11:22:28 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
2026-10-05 11:22:28 <1b>[0mScaling and centering 4 numeric features...<1b>[0m [preprocess]
2026-10-05 11:22:28 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
2026-10-05 11:22:28 <1b>[0mScaling and centering 4 numeric features...<1b>[0m [preprocess]
2026-10-05 11:22:28 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
2026-10-05 11:22:28 <1b>[0mApplying preprocessing to test data...<1b>[0m [preprocess]
2026-10-05 11:22:28 <1b>[0mScaling and centering 4 numeric features...<1b>[0m [preprocess]
2026-10-05 11:22:28 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
2026-10-05 11:22:28 <1b>[0mImputing missing values using get_mode (discrete) and mean (continuous)...<1b>[0m [preprocess]
2026-10-05 11:22:28 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
2026-10-05 11:22:28 <1b>[0mOne hot encoding g... ✔ [one_hot]
2026-10-05 11:22:28 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
2026-10-05 11:22:28 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:22:28 <1b>[0mUsing max n bins possible = 3.<1b>[0m [kfold]
2026-10-05 11:22:28 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:22:28 <1b>[0mUsing max n bins possible = 3.<1b>[0m [kfold]
2026-10-05 11:22:28 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Created file: /tmp/RtmpNpyEX1/working_dir/RtmpjfjDos/rtemis_super.json<1b>[0m [write_lines]
2026-10-05 11:22:28 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Created file: /tmp/RtmpNpyEX1/working_dir/RtmpjfjDos/rtemis_decom.json<1b>[0m [write_lines]
2026-10-05 11:22:28 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Created file: /tmp/RtmpNpyEX1/working_dir/RtmpjfjDos/rtemis_clust.json<1b>[0m [write_lines]
2026-10-05 11:22:28 ✖ rtemis_value_error<1b>[0m [.detect_config_kind]
2026-10-05 11:22:28 ✖ rtemis_value_error<1b>[0m [.detect_config_kind]
2026-10-05 11:22:29 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:22:29 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:22:29 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 11:22:29 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:22:29 <1b>[0mUsing max n bins possible = 3.<1b>[0m [kfold]
2026-10-05 11:22:29 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:22:29 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:22:29 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:22:29 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:22:29 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:22:29 <1b>[0mTraining GLM Regression...<1b>[0m [train]
2026-10-05 11:22:29 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLM<1b>[0m (Generalized Linear Model)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.73
<1b>[1m MSE<1b>[22m: 0.82
<1b>[1m RMSE<1b>[22m: 0.91
<1b>[1m R²<1b>[22m: 0.83
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.74
<1b>[1m MSE<1b>[22m: 1.03
<1b>[1m RMSE<1b>[22m: 1.01
<1b>[1m R²<1b>[22m: 0.77
2026-10-05 11:22:29 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.23 seconds.<1b>[0m [train]
2026-10-05 11:22:29 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:22:29 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:22:29 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:22:29 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:22:29 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:22:29 <1b>[0mTraining GLM Regression...<1b>[0m [train]
2026-10-05 11:22:29 <1b>[0mChecking data is ready for training...
2026-10-05 11:22:30 ✖ rtemis_missing_data<1b>[0m [check_supervised]
2026-10-05 11:22:30 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:22:30 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:22:30 <1b>[0mTraining set: 400 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:22:30 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: c(`_R_CHECK_LIMIT_CORES_` = 1) }<1b>[0m [get_n_workers]
2026-10-05 11:22:30 <1b>[0m<> Training GLM Regression using 3 independent folds...<1b>[0m [train]
2026-10-05 11:22:30 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:22:30 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Regression Model>
GLM (Generalized Linear Model)
⟳ Tested using 3 independent folds.
<Resampled Regression Training Metrics>
Showing mean (sd) across resamples.
MAE: 0.723 (0.010)
MSE: 0.833 (0.031)
RMSE: 0.913 (0.017)
R²: 0.830 (0.009)
<Resampled Regression Test Metrics>
Showing mean (sd) across resamples.
MAE: 0.747 (0.023)
MSE: 0.892 (0.065)
RMSE: 0.944 (0.034)
R²: 0.817 (0.017)
2026-10-05 11:22:30 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.63 seconds.<1b>[0m [train]
2026-10-05 11:22:30 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:22:30 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:22:30 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:22:30 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:22:30 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:22:30 <1b>[0mTraining GLM Classification...<1b>[0m [train]
2026-10-05 11:22:30 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLM<1b>[0m (Generalized Linear Model)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 44 1
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 44
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.978
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.998
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.018
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.093
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:22:31 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.24 seconds.<1b>[0m [train]
2026-10-05 11:22:31 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:22:31 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:22:31 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:22:31 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:22:31 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:22:31 <1b>[0mCalculating case weights using Inverse Frequency Weighting.<1b>[0m [ifw]
2026-10-05 11:22:31 <1b>[0mTraining GLM Classification...<1b>[0m [train]
2026-10-05 11:22:31 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLM<1b>[0m (Generalized Linear Model)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 44 1
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 44
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.978
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.998
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.018
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.093
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:22:31 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.17 seconds.<1b>[0m [train]
2026-10-05 11:22:31 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:22:31 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:22:31 <1b>[0mTraining set: 100 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:22:31 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: c(`_R_CHECK_LIMIT_CORES_` = 1) }<1b>[0m [get_n_workers]
2026-10-05 11:22:31 <1b>[0m<> Training GLM Classification using 3 independent folds...<1b>[0m [train]
2026-10-05 11:22:31 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:22:31 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 11:22:31 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Classification Model>
GLM (Generalized Linear Model)
⟳ Tested using 3 independent folds.
<Resampled Classification Training Metrics>
Aggregate Confusion Matrix across resamples.
Predicted
Reference virginica versicolor
virginica 99 1
versicolor 1 99
Showing mean (sd) across resamples.
Sensitivity: 0.990 (0.017)
Specificity: 0.990 (0.017)
Balanced Accuracy: 0.990 (0.017)
Ppv: 0.990 (0.017)
Npv: 0.990 (0.017)
F1: 0.990 (0.017)
Accuracy: 0.990 (0.017)
Auc: 0.998 (3.7e-03)
Brier Score: 0.007 (0.012)
<Resampled Classification Test Metrics>
Aggregate Confusion Matrix across resamples.
Predicted
Reference virginica versicolor
virginica 44 6
versicolor 5 45
Showing mean (sd) across resamples.
Sensitivity: 0.877 (0.125)
Specificity: 0.898 (0.095)
Balanced Accuracy: 0.888 (0.092)
Ppv: 0.898 (0.100)
Npv: 0.886 (0.118)
F1: 0.885 (0.096)
Accuracy: 0.888 (0.092)
Auc: 0.939 (0.062)
Brier Score: 0.101 (0.088)
2026-10-05 11:22:32 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.70 seconds.<1b>[0m [train]
2026-10-05 11:22:32 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:22:32 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:22:32 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:22:32 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:22:32 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:22:32 <1b>[0mTraining GLMNET Regression...<1b>[0m [train]
2026-10-05 11:22:32 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLMNET<1b>[0m (Elastic Net)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.73
<1b>[1m MSE<1b>[22m: 0.82
<1b>[1m RMSE<1b>[22m: 0.91
<1b>[1m R²<1b>[22m: 0.83
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.74
<1b>[1m MSE<1b>[22m: 1.02
<1b>[1m RMSE<1b>[22m: 1.01
<1b>[1m R²<1b>[22m: 0.77
2026-10-05 11:22:32 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.23 seconds.<1b>[0m [train]
2026-10-05 11:22:32 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:22:32 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:22:32 <1b>[0m▶ train GLMNET Regression<1b>[0m [session_render]
2026-10-05 11:22:32 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:22:32 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:22:32 <1b>[0m// Max workers: 2 { Algorithm: 1; Tuning: 2; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:22:32 <1b>[0m ▶ tune<1b>[0m [session_render]
2026-10-05 11:22:32 <1b>[0m▶<1b>[0m [tune_GridSearch]
2026-10-05 11:22:32 <1b>[0m<> Tuning GLMNET by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 11:22:32 <1b>[0m1 parameter combination x 5 resamples: 5 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 11:22:32 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mKFoldConfig<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m n<1b>[22m: 5
<1b>[1mstratify_var<1b>[22m: NULL
<1b>[1mstrat_n_bins<1b>[22m: 4
<1b>[1m id_strat<1b>[22m: NULL
<1b>[1m seed<1b>[22m: NULL
2026-10-05 11:22:33 <1b>[0mTuning using future (mirai_multisession); N workers: 2<1b>[0m [tune_GridSearch]
2026-10-05 11:22:33 ℹ Current future plan:<1b>[0m [tune_GridSearch]
mirai_multisession:
- args: function (..., workers = 2L, envir = parent.frame())
- tweaked: TRUE
- call: future::plan(strategy = requested_plan, workers = n_workers)
MiraiMultisessionFutureBackend:
Inherits: MiraiFutureBackend, MultiprocessFutureBackend, FutureBackend
UUID: 72f7a1c711c96856e76d73eed736556e
Number of workers: 2
Number of free workers: 2
Available cores: 2
Automatic garbage collection: FALSE
Early signaling: FALSE
Interrupts are enabled: TRUE
Maximum total size of globals: +Inf
Maximum total size of value: +Inf
Number of active futures: 0
Number of futures since start: 0 (0 created, 0 launched, 0 finished)
Total runtime of futures: 0 secs (NaN secs/finished future)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLMNET<1b>[0m (Elastic Net)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.77
<1b>[1m MSE<1b>[22m: 0.94
<1b>[1m RMSE<1b>[22m: 0.97
<1b>[1m R²<1b>[22m: 0.81
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mValidation Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.78
<1b>[1m MSE<1b>[22m: 0.91
<1b>[1m RMSE<1b>[22m: 0.96
<1b>[1m R²<1b>[22m: 0.83
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLMNET<1b>[0m (Elastic Net)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.75
<1b>[1m MSE<1b>[22m: 0.90
<1b>[1m RMSE<1b>[22m: 0.95
<1b>[1m R²<1b>[22m: 0.82
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mValidation Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.75
<1b>[1m MSE<1b>[22m: 0.87
<1b>[1m RMSE<1b>[22m: 0.93
<1b>[1m R²<1b>[22m: 0.80
2026-10-05 11:22:36 ℹ Running grid line #1/5...<1b>[0m [tune_GridSearch]
2026-10-05 11:22:36 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:22:36 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:22:36 <1b>[0m Training set: 285 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:22:36 <1b>[0mValidation set: 73 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:22:36 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:22:36 <1b>[0mTraining GLMNET Regression...<1b>[0m [train]
2026-10-05 11:22:40 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:22:40 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 3.98 seconds.<1b>[0m [train]
2026-10-05 11:22:40 ℹ Running grid line #2/5...<1b>[0m [tune_GridSearch]
2026-10-05 11:22:40 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:22:40 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:22:40 <1b>[0m Training set: 288 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:22:40 <1b>[0mValidation set: 70 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:22:40 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:22:40 <1b>[0mTraining GLMNET Regression...<1b>[0m [train]
2026-10-05 11:22:40 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:22:41 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.43 seconds.<1b>[0m [train]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLMNET<1b>[0m (Elastic Net)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.78
<1b>[1m MSE<1b>[22m: 0.93
<1b>[1m RMSE<1b>[22m: 0.97
<1b>[1m R²<1b>[22m: 0.81
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mValidation Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.68
<1b>[1m MSE<1b>[22m: 0.80
<1b>[1m RMSE<1b>[22m: 0.89
<1b>[1m R²<1b>[22m: 0.83
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLMNET<1b>[0m (Elastic Net)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.73
<1b>[1m MSE<1b>[22m: 0.88
<1b>[1m RMSE<1b>[22m: 0.94
<1b>[1m R²<1b>[22m: 0.82
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mValidation Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.79
<1b>[1m MSE<1b>[22m: 0.85
<1b>[1m RMSE<1b>[22m: 0.92
<1b>[1m R²<1b>[22m: 0.83
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLMNET<1b>[0m (Elastic Net)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.73
<1b>[1m MSE<1b>[22m: 0.82
<1b>[1m RMSE<1b>[22m: 0.91
<1b>[1m R²<1b>[22m: 0.83
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mValidation Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.82
<1b>[1m MSE<1b>[22m: 1.14
<1b>[1m RMSE<1b>[22m: 1.07
<1b>[1m R²<1b>[22m: 0.79
2026-10-05 11:22:36 ℹ Running grid line #3/5...<1b>[0m [tune_GridSearch]
2026-10-05 11:22:36 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:22:36 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:22:36 <1b>[0m Training set: 286 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:22:36 <1b>[0mValidation set: 72 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:22:36 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:22:36 <1b>[0mTraining GLMNET Regression...<1b>[0m [train]
2026-10-05 11:22:39 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:22:39 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 3.27 seconds.<1b>[0m [train]
2026-10-05 11:22:39 ℹ Running grid line #4/5...<1b>[0m [tune_GridSearch]
2026-10-05 11:22:39 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:22:39 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:22:39 <1b>[0m Training set: 288 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:22:39 <1b>[0mValidation set: 70 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:22:39 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:22:39 <1b>[0mTraining GLMNET Regression...<1b>[0m [train]
2026-10-05 11:22:40 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:22:40 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.35 seconds.<1b>[0m [train]
2026-10-05 11:22:40 ℹ Running grid line #5/5...<1b>[0m [tune_GridSearch]
2026-10-05 11:22:40 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:22:40 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:22:40 <1b>[0m Training set: 285 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:22:40 <1b>[0mValidation set: 73 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:22:40 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:22:40 <1b>[0mTraining GLMNET Regression...<1b>[0m [train]
2026-10-05 11:22:40 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:22:40 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.25 seconds.<1b>[0m [train]
2026-10-05 11:22:41 ℹ Extracting best lambda from GLMNET models...<1b>[0m [tune_GridSearch]
2026-10-05 11:22:41 <1b>[0mBest config to minimize mse:<1b>[0m [tune_GridSearch]
lambda: {} => 0.140014948415186
2026-10-05 11:22:41 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 8.78 seconds.<1b>[0m [tune_GridSearch]
2026-10-05 11:22:41 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 11:22:41 <1b>[0m ✔ tune (9.0 s)<1b>[0m [session_render]
2026-10-05 11:22:41 <1b>[0mTraining GLMNET Regression with tuned hyperparameters...<1b>[0m [train]
2026-10-05 11:22:41 <1b>[0m ▶ train_alg GLMNET<1b>[0m [session_render]
2026-10-05 11:22:41 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:22:41 ℹ NCOL(xm): 6<1b>[0m [train_]
2026-10-05 11:22:41 ℹ Updated hyperparameters[["penalty_factor"]] to all 1s.<1b>[0m [train_]
2026-10-05 11:22:41 <1b>[0m ✔ train_alg GLMNET (105 ms)<1b>[0m [session_render]
2026-10-05 11:22:41 <1b>[0m ▶ predict<1b>[0m [session_render]
2026-10-05 11:22:41 <1b>[0m ✔ predict (13 ms)<1b>[0m [session_render]
2026-10-05 11:22:41 <1b>[0m ▶ varimp GLMNET<1b>[0m [session_render]
2026-10-05 11:22:41 <1b>[0m ✔ varimp GLMNET (6 ms)<1b>[0m [session_render]
2026-10-05 11:22:41 <1b>[0m ▶ metrics<1b>[0m [session_render]
2026-10-05 11:22:41 <1b>[0m ✔ metrics (57 ms)<1b>[0m [session_render]
2026-10-05 11:22:41 <1b>[0m✔ train GLMNET Regression (9.2 s)<1b>[0m [session_render]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLMNET<1b>[0m (Elastic Net)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.75
<1b>[1m MSE<1b>[22m: 0.90
<1b>[1m RMSE<1b>[22m: 0.95
<1b>[1m R²<1b>[22m: 0.82
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.73
<1b>[1m MSE<1b>[22m: 0.95
<1b>[1m RMSE<1b>[22m: 0.98
<1b>[1m R²<1b>[22m: 0.79
2026-10-05 11:22:41 <1b>[0mModels trained:<1b>[0m [session_report]
2026-10-05 11:22:41 <1b>[0m tuning: 1 combos ⨉ 5 inner resamples = 5<1b>[0m [session_report]
2026-10-05 11:22:41 <1b>[0m + final: 1 per fit = 1<1b>[0m [session_report]
2026-10-05 11:22:41 <1b>[0m total = 6 models<1b>[0m [session_report]
2026-10-05 11:22:41 <1b>[0m 6 succeeded in 9.2 s<1b>[0m [session_report]
2026-10-05 11:22:41 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 9.26 seconds.<1b>[0m [train]
2026-10-05 11:22:41 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:22:41 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:22:41 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:22:41 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:22:41 <1b>[0m// Max workers: 2 { Algorithm: 1; Tuning: 2; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:22:41 ✖ rtemis_value_error<1b>[0m [tune_GridSearch]
2026-10-05 11:22:41 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:22:41 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:22:41 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:22:41 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:22:41 <1b>[0m// Max workers: 2 { Algorithm: 1; Tuning: 2; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:22:41 <1b>[0m<> Tuning GLMNET by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 11:22:41 <1b>[0m1 parameter combination x 5 resamples: 5 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 11:22:41 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:22:42 <1b>[0mTuning using mirai; N workers: 2<1b>[0m [tune_GridSearch]
======>------------------------ 20% | ETA: 26s
==============================> 100% | ETA: 0s
2026-10-05 11:22:49 <1b>[0mBest config to minimize mse:<1b>[0m [tune_GridSearch]
lambda: {} => 0.144320781405745
2026-10-05 11:22:49 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 11:22:49 <1b>[0mTraining GLMNET Regression with tuned hyperparameters...<1b>[0m [train]
2026-10-05 11:22:49 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLMNET<1b>[0m (Elastic Net)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.76
<1b>[1m MSE<1b>[22m: 0.90
<1b>[1m RMSE<1b>[22m: 0.95
<1b>[1m R²<1b>[22m: 0.82
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.73
<1b>[1m MSE<1b>[22m: 0.95
<1b>[1m RMSE<1b>[22m: 0.98
<1b>[1m R²<1b>[22m: 0.79
2026-10-05 11:22:49 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 8.01 seconds.<1b>[0m [train]
2026-10-05 11:22:50 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:22:50 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:22:50 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:22:50 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:22:50 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:22:50 <1b>[0m<> Tuning GLMNET by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 11:22:50 <1b>[0m2 parameter combinations x 5 resamples: 10 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 11:22:50 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:22:50 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
\ 7/10 ETA: 1s | Tuning... (10 combinations)
2026-10-05 11:22:53 <1b>[0mBest config to minimize mse:<1b>[0m [tune_GridSearch]
alpha: {0, 1} => 1
lambda: {} => 0.134770240620737
2026-10-05 11:22:53 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 11:22:53 <1b>[0mTraining GLMNET Regression with tuned hyperparameters...<1b>[0m [train]
2026-10-05 11:22:53 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLMNET<1b>[0m (Elastic Net)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.75
<1b>[1m MSE<1b>[22m: 0.89
<1b>[1m RMSE<1b>[22m: 0.95
<1b>[1m R²<1b>[22m: 0.82
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.73
<1b>[1m MSE<1b>[22m: 0.96
<1b>[1m RMSE<1b>[22m: 0.98
<1b>[1m R²<1b>[22m: 0.79
2026-10-05 11:22:53 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 3.33 seconds.<1b>[0m [train]
2026-10-05 11:22:53 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:22:53 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:22:53 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:22:53 <1b>[0mTuning parallelization enabled.<1b>[0m [get_n_workers]
2026-10-05 11:22:53 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:22:53 <1b>[0m<> Training GLMNET Regression using 3 independent folds...<1b>[0m [train]
2026-10-05 11:22:53 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:23:03 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Regression Model>
GLMNET (Elastic Net)
⚙ Tuned using exhaustive grid search.
⟳ Tested using 3 independent folds.
<Resampled Regression Training Metrics>
Showing mean (sd) across resamples.
MAE: 0.760 (0.030)
MSE: 0.910 (0.042)
RMSE: 0.954 (0.022)
R²: 0.816 (0.011)
<Resampled Regression Test Metrics>
Showing mean (sd) across resamples.
MAE: 0.766 (0.056)
MSE: 0.921 (0.077)
RMSE: 0.959 (0.040)
R²: 0.813 (0.021)
2026-10-05 11:23:03 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 9.87 seconds.<1b>[0m [train]
2026-10-05 11:23:03 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:03 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:23:03 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:03 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:03 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:23:03 <1b>[0mCalculating case weights using Inverse Frequency Weighting.<1b>[0m [ifw]
2026-10-05 11:23:03 <1b>[0mTraining GLMNET Classification...<1b>[0m [train]
2026-10-05 11:23:03 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLMNET<1b>[0m (Elastic Net)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 43 2
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 2 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.956
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.998
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.024
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.055
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:23:03 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.13 seconds.<1b>[0m [train]
2026-10-05 11:23:03 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:03 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:23:03 <1b>[0mTraining set: 135 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:03 <1b>[0m Test set: 15 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:03 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:23:03 <1b>[0m<> Tuning GLMNET by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 11:23:03 <1b>[0m1 parameter combination x 5 resamples: 5 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 11:23:03 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:23:03 <1b>[0mUsing max n bins possible = 3.<1b>[0m [kfold]
2026-10-05 11:23:03 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
\ 3/5 ETA: 3s | Tuning... (5 combinations)
2026-10-05 11:23:09 <1b>[0mBest config to maximize balanced_accuracy:<1b>[0m [tune_GridSearch]
lambda: {} => 0.00821962912592355
2026-10-05 11:23:09 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 11:23:09 <1b>[0mCalculating case weights using Inverse Frequency Weighting.<1b>[0m [ifw]
2026-10-05 11:23:09 <1b>[0mTraining GLMNET Classification with tuned hyperparameters...<1b>[0m [train]
2026-10-05 11:23:09 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLMNET<1b>[0m (Elastic Net)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 45 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 42 3
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 2 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.963
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.963
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.963
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 0.933 0.956
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 0.978 0.967
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000 0.956 0.961
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 0.955 0.935
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 0.967 0.978
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000 0.944 0.945
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 5 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 5 0
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.933
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.933
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.933
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 1.000 0.800
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 0.900 1.000
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000 0.950 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 0.833 1.000
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 1.000 0.909
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000 0.909 0.889
2026-10-05 11:23:10 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 6.63 seconds.<1b>[0m [train]
2026-10-05 11:23:10 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:10 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:23:10 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:10 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:10 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:23:10 <1b>[0mTraining GAM Regression...<1b>[0m [train]
2026-10-05 11:23:10 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGAM<1b>[0m (Generalized Additive Model)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.72
<1b>[1m MSE<1b>[22m: 0.80
<1b>[1m RMSE<1b>[22m: 0.89
<1b>[1m R²<1b>[22m: 0.84
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.75
<1b>[1m MSE<1b>[22m: 1.01
<1b>[1m RMSE<1b>[22m: 1.00
<1b>[1m R²<1b>[22m: 0.77
2026-10-05 11:23:10 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.45 seconds.<1b>[0m [train]
2026-10-05 11:23:10 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:10 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:23:10 <1b>[0mTraining set: 358 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:10 <1b>[0m Test set: 42 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:10 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:23:10 <1b>[0mTraining GAM Regression...<1b>[0m [train]
2026-10-05 11:23:10 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGAM<1b>[0m (Generalized Additive Model)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 1.49
<1b>[1m MSE<1b>[22m: 2.95
<1b>[1m RMSE<1b>[22m: 1.72
<1b>[1m R²<1b>[22m: 0.40
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 1.26
<1b>[1m MSE<1b>[22m: 2.20
<1b>[1m RMSE<1b>[22m: 1.48
<1b>[1m R²<1b>[22m: 0.51
2026-10-05 11:23:11 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.48 seconds.<1b>[0m [train]
2026-10-05 11:23:11 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:11 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:23:11 <1b>[0mTraining set: 358 cases x 1 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:11 <1b>[0m Test set: 42 cases x 1 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:11 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:23:11 <1b>[0mTraining GAM Regression...<1b>[0m [train]
2026-10-05 11:23:11 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGAM<1b>[0m (Generalized Additive Model)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 1.37
<1b>[1m MSE<1b>[22m: 2.80
<1b>[1m RMSE<1b>[22m: 1.67
<1b>[1m R²<1b>[22m: 0.43
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 1.16
<1b>[1m MSE<1b>[22m: 1.93
<1b>[1m RMSE<1b>[22m: 1.39
<1b>[1m R²<1b>[22m: 0.57
2026-10-05 11:23:11 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.16 seconds.<1b>[0m [train]
2026-10-05 11:23:11 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:11 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:23:11 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:11 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:11 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: c(`_R_CHECK_LIMIT_CORES_` = 1); Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:23:11 <1b>[0m<> Tuning GAM by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 11:23:11 <1b>[0m3 parameter combinations x 5 resamples: 15 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 11:23:11 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:23:11 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
2026-10-05 11:23:15 <1b>[0mBest config to minimize mse:<1b>[0m [tune_GridSearch]
k: {3, 5, 7} => 3
2026-10-05 11:23:15 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 11:23:15 <1b>[0mTraining GAM Regression with tuned hyperparameters...<1b>[0m [train]
2026-10-05 11:23:15 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGAM<1b>[0m (Generalized Additive Model)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.72
<1b>[1m MSE<1b>[22m: 0.82
<1b>[1m RMSE<1b>[22m: 0.91
<1b>[1m R²<1b>[22m: 0.83
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.74
<1b>[1m MSE<1b>[22m: 1.02
<1b>[1m RMSE<1b>[22m: 1.01
<1b>[1m R²<1b>[22m: 0.77
2026-10-05 11:23:16 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 4.86 seconds.<1b>[0m [train]
2026-10-05 11:23:16 ✖ rtemis_dim_error<1b>[0m [predict_supervised_]
2026-10-05 11:23:16 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:16 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:23:16 <1b>[0mTraining set: 400 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:16 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: c(`_R_CHECK_LIMIT_CORES_` = 1) }<1b>[0m [get_n_workers]
2026-10-05 11:23:16 <1b>[0m<> Training GAM Regression using 3 independent folds...<1b>[0m [train]
2026-10-05 11:23:16 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:23:17 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Regression Model>
GAM (Generalized Additive Model)
⟳ Tested using 3 independent folds.
<Resampled Regression Training Metrics>
Showing mean (sd) across resamples.
MAE: 0.709 (0.025)
MSE: 0.796 (0.067)
RMSE: 0.891 (0.038)
R²: 0.838 (0.012)
<Resampled Regression Test Metrics>
Showing mean (sd) across resamples.
MAE: 0.759 (0.059)
MSE: 0.908 (0.136)
RMSE: 0.951 (0.070)
R²: 0.815 (0.024)
2026-10-05 11:23:17 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 1.14 seconds.<1b>[0m [train]
2026-10-05 11:23:17 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:17 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:23:17 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:17 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:17 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:23:17 <1b>[0mTraining GAM Classification...<1b>[0m [train]
2026-10-05 11:23:17 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGAM<1b>[0m (Generalized Additive Model)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 45 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 45
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Auc<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 2.2e-06
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.100
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:23:18 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.52 seconds.<1b>[0m [train]
2026-10-05 11:23:18 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:18 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:23:18 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:18 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:18 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:23:18 <1b>[0mCalculating case weights using Inverse Frequency Weighting.<1b>[0m [ifw]
2026-10-05 11:23:18 <1b>[0mTraining GAM Classification...<1b>[0m [train]
2026-10-05 11:23:18 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGAM<1b>[0m (Generalized Additive Model)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 45 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 45
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Auc<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 2.2e-06
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.100
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:23:18 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.48 seconds.<1b>[0m [train]
2026-10-05 11:23:18 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:18 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:23:18 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:18 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:18 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:23:18 <1b>[0mTraining LinearSVM Regression...<1b>[0m [train]
2026-10-05 11:23:18 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:18 <1b>[0mOne hot encoding g... ✔ [one_hot]
2026-10-05 11:23:18 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLinearSVM<1b>[0m (Support Vector Machine with Linear Kernel)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.72
<1b>[1m MSE<1b>[22m: 0.83
<1b>[1m RMSE<1b>[22m: 0.91
<1b>[1m R²<1b>[22m: 0.83
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.75
<1b>[1m MSE<1b>[22m: 1.02
<1b>[1m RMSE<1b>[22m: 1.01
<1b>[1m R²<1b>[22m: 0.77
2026-10-05 11:23:18 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.33 seconds.<1b>[0m [train]
2026-10-05 11:23:18 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:18 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:23:18 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:18 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:18 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:23:18 <1b>[0m<> Tuning LinearSVM by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 11:23:18 <1b>[0m2 parameter combinations x 5 resamples: 10 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 11:23:18 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:23:18 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
2026-10-05 11:23:22 <1b>[0mBest config to minimize mse:<1b>[0m [tune_GridSearch]
cost: {1, 10} => 1
2026-10-05 11:23:22 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 11:23:22 <1b>[0mTraining LinearSVM Regression with tuned hyperparameters...<1b>[0m [train]
2026-10-05 11:23:22 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:22 <1b>[0mOne hot encoding g... ✔ [one_hot]
2026-10-05 11:23:22 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLinearSVM<1b>[0m (Support Vector Machine with Linear Kernel)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.72
<1b>[1m MSE<1b>[22m: 0.83
<1b>[1m RMSE<1b>[22m: 0.91
<1b>[1m R²<1b>[22m: 0.83
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.75
<1b>[1m MSE<1b>[22m: 1.02
<1b>[1m RMSE<1b>[22m: 1.01
<1b>[1m R²<1b>[22m: 0.77
2026-10-05 11:23:22 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 3.86 seconds.<1b>[0m [train]
2026-10-05 11:23:22 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:22 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:23:22 <1b>[0mTraining set: 400 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:22 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: c(`_R_CHECK_LIMIT_CORES_` = 1) }<1b>[0m [get_n_workers]
2026-10-05 11:23:22 <1b>[0m<> Training LinearSVM Regression using 3 independent folds...<1b>[0m [train]
2026-10-05 11:23:22 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:23:23 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Regression Model>
LinearSVM (Support Vector Machine with Linear Kernel)
⟳ Tested using 3 independent folds.
<Resampled Regression Training Metrics>
Showing mean (sd) across resamples.
MAE: 0.723 (0.021)
MSE: 0.845 (0.022)
RMSE: 0.919 (0.012)
R²: 0.827 (0.008)
<Resampled Regression Test Metrics>
Showing mean (sd) across resamples.
MAE: 0.741 (0.043)
MSE: 0.875 (0.056)
RMSE: 0.935 (0.030)
R²: 0.821 (0.019)
2026-10-05 11:23:23 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.83 seconds.<1b>[0m [train]
2026-10-05 11:23:23 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:23 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:23:23 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:23 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:23 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:23:23 <1b>[0mTraining LinearSVM Classification...<1b>[0m [train]
2026-10-05 11:23:23 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:23 <1b>[0mOne hot encoding gn... ✔ [one_hot]
2026-10-05 11:23:23 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLinearSVM<1b>[0m (Support Vector Machine with Linear Kernel)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 45 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 2 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.957
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.999
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.023
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.041
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:23:23 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.17 seconds.<1b>[0m [train]
2026-10-05 11:23:23 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:23 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:23:23 <1b>[0mTraining set: 135 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:23 <1b>[0m Test set: 15 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:23 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:23:23 <1b>[0mTraining LinearSVM Classification...<1b>[0m [train]
2026-10-05 11:23:23 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLinearSVM<1b>[0m (Support Vector Machine with Linear Kernel)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 45 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 42 3
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 1 44
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.970
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.970
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.970
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 0.933 0.978
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 0.989 0.967
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000 0.961 0.972
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 0.977 0.936
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 0.967 0.989
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000 0.955 0.957
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 5 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 5 0
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 0 5
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 1.000
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000 1.000 1.000
2026-10-05 11:23:24 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.17 seconds.<1b>[0m [train]
2026-10-05 11:23:24 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:24 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:23:24 <1b>[0mTraining set: 100 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:24 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:23:24 <1b>[0m<> Training LinearSVM Classification using 3 independent folds...<1b>[0m [train]
2026-10-05 11:23:24 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:23:24 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 11:23:24 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Classification Model>
LinearSVM (Support Vector Machine with Linear Kernel)
⟳ Tested using 3 independent folds.
<Resampled Classification Training Metrics>
Aggregate Confusion Matrix across resamples.
Predicted
Reference virginica versicolor
virginica 100 0
versicolor 6 94
Showing mean (sd) across resamples.
Sensitivity: 1.000 (0.000)
Specificity: 0.940 (0.030)
Balanced Accuracy: 0.970 (0.015)
Ppv: 0.944 (0.027)
Npv: 1.000 (0.000)
F1: 0.971 (0.014)
Accuracy: 0.970 (0.015)
Auc: 0.998 (4.9e-04)
Brier Score: 0.029 (0.006)
<Resampled Classification Test Metrics>
Aggregate Confusion Matrix across resamples.
Predicted
Reference virginica versicolor
virginica 49 1
versicolor 3 47
Showing mean (sd) across resamples.
Sensitivity: 0.979 (0.036)
Specificity: 0.941 (0.059)
Balanced Accuracy: 0.960 (0.016)
Ppv: 0.946 (0.053)
Npv: 0.980 (0.034)
F1: 0.961 (0.015)
Accuracy: 0.960 (0.016)
Auc: 0.998 (4e-03)
Brier Score: 0.034 (1.7e-03)
2026-10-05 11:23:24 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.47 seconds.<1b>[0m [train]
2026-10-05 11:23:24 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:24 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:23:24 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:24 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:24 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:23:24 <1b>[0mTraining RadialSVM Regression...<1b>[0m [train]
2026-10-05 11:23:24 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:24 <1b>[0mOne hot encoding g... ✔ [one_hot]
2026-10-05 11:23:24 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mRadialSVM<1b>[0m (Support Vector Machine with Radial Kernel)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.73
<1b>[1m MSE<1b>[22m: 0.84
<1b>[1m RMSE<1b>[22m: 0.92
<1b>[1m R²<1b>[22m: 0.83
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.75
<1b>[1m MSE<1b>[22m: 1.01
<1b>[1m RMSE<1b>[22m: 1.01
<1b>[1m R²<1b>[22m: 0.77
2026-10-05 11:23:24 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.22 seconds.<1b>[0m [train]
2026-10-05 11:23:24 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:24 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:23:24 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:24 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:24 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: c(`_R_CHECK_LIMIT_CORES_` = 1); Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:23:24 <1b>[0m<> Tuning RadialSVM by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 11:23:24 <1b>[0m3 parameter combinations x 5 resamples: 15 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 11:23:24 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:23:24 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
2026-10-05 11:23:28 <1b>[0mBest config to minimize mse:<1b>[0m [tune_GridSearch]
cost: {1, 10, 100} => 1
2026-10-05 11:23:28 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 11:23:28 <1b>[0mTraining RadialSVM Regression with tuned hyperparameters...<1b>[0m [train]
2026-10-05 11:23:28 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:28 <1b>[0mOne hot encoding g... ✔ [one_hot]
2026-10-05 11:23:28 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mRadialSVM<1b>[0m (Support Vector Machine with Radial Kernel)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.73
<1b>[1m MSE<1b>[22m: 0.84
<1b>[1m RMSE<1b>[22m: 0.92
<1b>[1m R²<1b>[22m: 0.83
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.75
<1b>[1m MSE<1b>[22m: 1.01
<1b>[1m RMSE<1b>[22m: 1.01
<1b>[1m R²<1b>[22m: 0.77
2026-10-05 11:23:28 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 3.42 seconds.<1b>[0m [train]
2026-10-05 11:23:28 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:28 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:23:28 <1b>[0mTraining set: 400 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:28 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:23:28 <1b>[0m<> Training RadialSVM Regression using 3 independent folds...<1b>[0m [train]
2026-10-05 11:23:28 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:23:28 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Regression Model>
RadialSVM (Support Vector Machine with Radial Kernel)
⟳ Tested using 3 independent folds.
<Resampled Regression Training Metrics>
Showing mean (sd) across resamples.
MAE: 0.729 (0.014)
MSE: 0.861 (0.011)
RMSE: 0.928 (0.006)
R²: 0.824 (2.9e-03)
<Resampled Regression Test Metrics>
Showing mean (sd) across resamples.
MAE: 0.768 (0.032)
MSE: 0.938 (0.030)
RMSE: 0.968 (0.016)
R²: 0.809 (0.006)
2026-10-05 11:23:28 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.60 seconds.<1b>[0m [train]
2026-10-05 11:23:28 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:28 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:23:28 <1b>[0mTraining set: 400 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:28 <1b>[0mTuning parallelization enabled.<1b>[0m [get_n_workers]
2026-10-05 11:23:28 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:23:28 <1b>[0m<> Training RadialSVM Regression using 3 independent folds...<1b>[0m [train]
2026-10-05 11:23:28 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:23:34 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Regression Model>
RadialSVM (Support Vector Machine with Radial Kernel)
⚙ Tuned using exhaustive grid search.
⟳ Tested using 3 independent folds.
<Resampled Regression Training Metrics>
Showing mean (sd) across resamples.
MAE: 0.713 (0.024)
MSE: 0.837 (0.051)
RMSE: 0.915 (0.028)
R²: 0.829 (0.011)
<Resampled Regression Test Metrics>
Showing mean (sd) across resamples.
MAE: 0.787 (0.005)
MSE: 0.963 (3.8e-03)
RMSE: 0.981 (1.9e-03)
R²: 0.803 (0.007)
2026-10-05 11:23:34 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 5.85 seconds.<1b>[0m [train]
2026-10-05 11:23:34 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:34 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:23:34 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:34 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:34 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:23:34 <1b>[0mTraining RadialSVM Classification...<1b>[0m [train]
2026-10-05 11:23:34 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:34 <1b>[0mOne hot encoding gn... ✔ [one_hot]
2026-10-05 11:23:34 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mRadialSVM<1b>[0m (Support Vector Machine with Radial Kernel)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 42 3
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 2 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.933
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.944
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.955
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.935
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.944
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.944
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.995
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.035
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.059
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:23:34 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.12 seconds.<1b>[0m [train]
2026-10-05 11:23:34 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:34 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:23:34 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:35 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:35 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:23:35 <1b>[0m<> Tuning RadialSVM by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 11:23:35 <1b>[0m2 parameter combinations x 5 resamples: 10 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 11:23:35 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:23:35 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 11:23:35 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
2026-10-05 11:23:36 <1b>[0mBest config to maximize balanced_accuracy:<1b>[0m [tune_GridSearch]
cost: {1, 10} => 10
2026-10-05 11:23:36 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 11:23:36 <1b>[0mTraining RadialSVM Classification with tuned hyperparameters...<1b>[0m [train]
2026-10-05 11:23:36 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:36 <1b>[0mOne hot encoding gn... ✔ [one_hot]
2026-10-05 11:23:36 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mRadialSVM<1b>[0m (Support Vector Machine with Radial Kernel)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 44 1
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 2 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.967
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.957
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.977
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.967
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.967
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.999
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.022
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.049
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:23:36 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 1.28 seconds.<1b>[0m [train]
2026-10-05 11:23:36 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:36 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:23:36 <1b>[0mTraining set: 100 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:36 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:23:36 <1b>[0m<> Training RadialSVM Classification using 3 independent folds...<1b>[0m [train]
2026-10-05 11:23:36 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:23:36 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 11:23:36 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Classification Model>
RadialSVM (Support Vector Machine with Radial Kernel)
⟳ Tested using 3 independent folds.
<Resampled Classification Training Metrics>
Aggregate Confusion Matrix across resamples.
Predicted
Reference virginica versicolor
virginica 93 7
versicolor 4 96
Showing mean (sd) across resamples.
Sensitivity: 0.930 (0.018)
Specificity: 0.960 (0.045)
Balanced Accuracy: 0.945 (0.016)
Ppv: 0.961 (0.043)
Npv: 0.932 (0.014)
F1: 0.945 (0.015)
Accuracy: 0.945 (0.016)
Auc: 0.992 (4.2e-03)
Brier Score: 0.044 (0.008)
<Resampled Classification Test Metrics>
Aggregate Confusion Matrix across resamples.
Predicted
Reference virginica versicolor
virginica 45 5
versicolor 4 46
Showing mean (sd) across resamples.
Sensitivity: 0.901 (0.067)
Specificity: 0.920 (0.033)
Balanced Accuracy: 0.911 (0.028)
Ppv: 0.920 (0.027)
Npv: 0.906 (0.055)
F1: 0.909 (0.032)
Accuracy: 0.911 (0.028)
Auc: 0.979 (0.021)
Brier Score: 0.056 (0.020)
2026-10-05 11:23:36 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.52 seconds.<1b>[0m [train]
2026-10-05 11:23:36 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:36 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:23:36 <1b>[0mTraining set: 100 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:36 <1b>[0mTuning parallelization enabled.<1b>[0m [get_n_workers]
2026-10-05 11:23:36 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:23:36 <1b>[0m<> Training RadialSVM Classification using 3 independent folds...<1b>[0m [train]
2026-10-05 11:23:36 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:23:36 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 11:23:41 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Classification Model>
RadialSVM (Support Vector Machine with Radial Kernel)
⚙ Tuned using exhaustive grid search.
⟳ Tested using 3 independent folds.
<Resampled Classification Training Metrics>
Aggregate Confusion Matrix across resamples.
Predicted
Reference virginica versicolor
virginica 97 3
versicolor 2 98
Showing mean (sd) across resamples.
Sensitivity: 0.970 (5.1e-04)
Specificity: 0.980 (0.017)
Balanced Accuracy: 0.975 (0.009)
Ppv: 0.980 (0.017)
Npv: 0.970 (1e-03)
F1: 0.975 (0.009)
Accuracy: 0.975 (0.009)
Auc: 0.998 (1.4e-03)
Brier Score: 0.024 (0.006)
<Resampled Classification Test Metrics>
Aggregate Confusion Matrix across resamples.
Predicted
Reference virginica versicolor
virginica 47 3
versicolor 3 47
Showing mean (sd) across resamples.
Sensitivity: 0.941 (0.102)
Specificity: 0.939 (0.063)
Balanced Accuracy: 0.940 (0.029)
Ppv: 0.944 (0.056)
Npv: 0.950 (0.087)
F1: 0.939 (0.034)
Accuracy: 0.940 (0.029)
Auc: 0.989 (0.007)
Brier Score: 0.048 (0.007)
2026-10-05 11:23:41 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 4.51 seconds.<1b>[0m [train]
2026-10-05 11:23:41 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:41 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:23:41 <1b>[0mTraining set: 135 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:41 <1b>[0m Test set: 15 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:41 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:23:41 <1b>[0mTraining RadialSVM Classification...<1b>[0m [train]
2026-10-05 11:23:41 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mRadialSVM<1b>[0m (Support Vector Machine with Radial Kernel)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 45 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 39 6
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 6 39
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.911
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.911
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.911
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 0.867 0.867
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 0.933 0.933
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000 0.900 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 0.867 0.867
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 0.933 0.933
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000 0.867 0.867
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 5 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 5 0
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 2 3
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.867
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.861
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.867
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 1.000 0.600
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 0.800 1.000
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000 0.900 0.800
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 0.714 1.000
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 1.000 0.833
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000 0.833 0.750
2026-10-05 11:23:41 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.20 seconds.<1b>[0m [train]
2026-10-05 11:23:41 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:41 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:23:41 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:41 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:41 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:23:41 <1b>[0mTraining CART Regression...<1b>[0m [train]
2026-10-05 11:23:41 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mCART<1b>[0m (Classification and Regression Trees)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.84
<1b>[1m MSE<1b>[22m: 1.13
<1b>[1m RMSE<1b>[22m: 1.06
<1b>[1m R²<1b>[22m: 0.77
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 1.08
<1b>[1m MSE<1b>[22m: 1.80
<1b>[1m RMSE<1b>[22m: 1.34
<1b>[1m R²<1b>[22m: 0.60
2026-10-05 11:23:41 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.17 seconds.<1b>[0m [train]
2026-10-05 11:23:41 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:41 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:23:41 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:41 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:41 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:23:41 <1b>[0m<> Tuning CART by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 11:23:41 <1b>[0m2 parameter combinations x 5 resamples: 10 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 11:23:41 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:23:41 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
2026-10-05 11:23:43 <1b>[0mBest config to minimize mse:<1b>[0m [tune_GridSearch]
maxdepth: {2, 3} => 3
2026-10-05 11:23:43 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 11:23:43 <1b>[0mTraining CART Regression with tuned hyperparameters...<1b>[0m [train]
2026-10-05 11:23:43 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mCART<1b>[0m (Classification and Regression Trees)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.93
<1b>[1m MSE<1b>[22m: 1.43
<1b>[1m RMSE<1b>[22m: 1.19
<1b>[1m R²<1b>[22m: 0.71
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 1.05
<1b>[1m MSE<1b>[22m: 1.59
<1b>[1m RMSE<1b>[22m: 1.26
<1b>[1m R²<1b>[22m: 0.64
2026-10-05 11:23:43 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 1.41 seconds.<1b>[0m [train]
2026-10-05 11:23:43 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:43 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:23:43 <1b>[0mTraining set: 400 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:43 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:23:43 <1b>[0m<> Training CART Regression using 3 independent folds...<1b>[0m [train]
2026-10-05 11:23:43 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:23:43 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Regression Model>
CART (Classification and Regression Trees)
⟳ Tested using 3 independent folds.
<Resampled Regression Training Metrics>
Showing mean (sd) across resamples.
MAE: 0.788 (0.043)
MSE: 0.981 (0.063)
RMSE: 0.990 (0.032)
R²: 0.800 (0.011)
<Resampled Regression Test Metrics>
Showing mean (sd) across resamples.
MAE: 1.045 (0.051)
MSE: 1.710 (0.156)
RMSE: 1.307 (0.059)
R²: 0.651 (0.033)
2026-10-05 11:23:43 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.43 seconds.<1b>[0m [train]
2026-10-05 11:23:43 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:43 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:23:43 <1b>[0mTraining set: 400 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:43 <1b>[0mTuning parallelization enabled.<1b>[0m [get_n_workers]
2026-10-05 11:23:43 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:23:43 <1b>[0m<> Training CART Regression using 3 independent folds...<1b>[0m [train]
2026-10-05 11:23:43 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:23:49 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Regression Model>
CART (Classification and Regression Trees)
⚙ Tuned using exhaustive grid search.
⟳ Tested using 3 independent folds.
<Resampled Regression Training Metrics>
Showing mean (sd) across resamples.
MAE: 1.134 (0.054)
MSE: 1.944 (0.122)
RMSE: 1.394 (0.044)
R²: 0.604 (0.021)
<Resampled Regression Test Metrics>
Showing mean (sd) across resamples.
MAE: 1.223 (0.050)
MSE: 2.403 (0.145)
RMSE: 1.550 (0.047)
R²: 0.510 (0.025)
2026-10-05 11:23:49 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 5.38 seconds.<1b>[0m [train]
2026-10-05 11:23:49 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:49 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:23:49 <1b>[0mTraining set: 400 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:49 <1b>[0mTuning parallelization enabled.<1b>[0m [get_n_workers]
2026-10-05 11:23:49 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:23:49 <1b>[0m<> Training CART Regression using 3 independent folds...<1b>[0m [train]
2026-10-05 11:23:49 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:23:52 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Regression Model>
CART (Classification and Regression Trees)
⚙ Tuned using exhaustive grid search.
⟳ Tested using 3 independent folds.
<Resampled Regression Training Metrics>
Showing mean (sd) across resamples.
MAE: 0.804 (0.012)
MSE: 1.022 (0.027)
RMSE: 1.011 (0.013)
R²: 0.791 (0.011)
<Resampled Regression Test Metrics>
Showing mean (sd) across resamples.
MAE: 1.070 (0.022)
MSE: 1.810 (0.049)
RMSE: 1.345 (0.018)
R²: 0.629 (0.028)
2026-10-05 11:23:52 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 3.39 seconds.<1b>[0m [train]
2026-10-05 11:23:52 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:52 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:23:52 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:52 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:52 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: c(`_R_CHECK_LIMIT_CORES_` = 1); Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:23:52 <1b>[0m<> Tuning CART by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 11:23:52 <1b>[0m2 parameter combinations x 5 resamples: 10 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 11:23:52 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:23:52 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 11:23:52 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
2026-10-05 11:23:53 <1b>[0mBest config to maximize balanced_accuracy:<1b>[0m [tune_GridSearch]
maxdepth: {1, 2} => 2
2026-10-05 11:23:53 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 11:23:53 <1b>[0mTraining CART Classification with tuned hyperparameters...<1b>[0m [train]
2026-10-05 11:23:53 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mCART<1b>[0m (Classification and Regression Trees)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 45 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 2 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.957
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.987
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.020
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.096
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:23:53 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.78 seconds.<1b>[0m [train]
2026-10-05 11:23:53 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:53 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:23:53 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:53 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:53 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:23:53 <1b>[0mCalculating case weights using Inverse Frequency Weighting.<1b>[0m [ifw]
2026-10-05 11:23:53 <1b>[0mTraining CART Classification...<1b>[0m [train]
2026-10-05 11:23:53 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mCART<1b>[0m (Classification and Regression Trees)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 45 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 45
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Auc<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.000
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.100
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:23:53 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.11 seconds.<1b>[0m [train]
2026-10-05 11:23:53 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:53 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:23:53 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:53 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:53 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:23:53 <1b>[0m<> Tuning CART by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 11:23:53 <1b>[0m2 parameter combinations x 5 resamples: 10 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 11:23:53 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:23:53 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 11:23:53 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
2026-10-05 11:23:54 <1b>[0mBest config to maximize balanced_accuracy:<1b>[0m [tune_GridSearch]
maxdepth: {1, 2} => 2
2026-10-05 11:23:54 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 11:23:54 <1b>[0mTraining CART Classification with tuned hyperparameters...<1b>[0m [train]
2026-10-05 11:23:54 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mCART<1b>[0m (Classification and Regression Trees)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 45 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 2 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.957
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.987
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.020
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.096
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:23:54 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.76 seconds.<1b>[0m [train]
2026-10-05 11:23:54 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:54 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:23:54 <1b>[0mTraining set: 100 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:54 <1b>[0mTuning parallelization enabled.<1b>[0m [get_n_workers]
2026-10-05 11:23:54 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:23:54 <1b>[0m<> Training CART Classification using 3 independent folds...<1b>[0m [train]
2026-10-05 11:23:54 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:23:54 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 11:23:56 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Classification Model>
CART (Classification and Regression Trees)
⚙ Tuned using exhaustive grid search.
⟳ Tested using 3 independent folds.
<Resampled Classification Training Metrics>
Aggregate Confusion Matrix across resamples.
Predicted
Reference virginica versicolor
virginica 94 6
versicolor 5 95
Showing mean (sd) across resamples.
Sensitivity: 0.941 (0.059)
Specificity: 0.949 (0.063)
Balanced Accuracy: 0.945 (0.008)
Ppv: 0.954 (0.056)
Npv: 0.945 (0.053)
F1: 0.945 (0.008)
Accuracy: 0.945 (0.008)
Auc: 0.946 (0.008)
Brier Score: 0.050 (0.006)
<Resampled Classification Test Metrics>
Aggregate Confusion Matrix across resamples.
Predicted
Reference virginica versicolor
virginica 46 4
versicolor 4 46
Showing mean (sd) across resamples.
Sensitivity: 0.920 (0.089)
Specificity: 0.920 (0.089)
Balanced Accuracy: 0.920 (0.015)
Ppv: 0.929 (0.075)
Npv: 0.929 (0.075)
F1: 0.920 (0.017)
Accuracy: 0.920 (0.015)
Auc: 0.911 (0.028)
Brier Score: 0.075 (0.011)
2026-10-05 11:23:56 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 2.60 seconds.<1b>[0m [train]
2026-10-05 11:23:56 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:56 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:23:56 <1b>[0mTraining set: 135 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:56 <1b>[0m Test set: 15 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:56 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:23:56 <1b>[0mTraining CART Classification...<1b>[0m [train]
2026-10-05 11:23:56 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mCART<1b>[0m (Classification and Regression Trees)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 45 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 44 1
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 0 45
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.993
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.993
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.993
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 0.978 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 1.000 0.989
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000 0.989 0.994
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 1.000 0.978
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 0.989 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000 0.989 0.989
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 5 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 5 0
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 0 5
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 1.000
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000 1.000 1.000
2026-10-05 11:23:57 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.16 seconds.<1b>[0m [train]
2026-10-05 11:23:57 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:57 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:23:57 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:57 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:57 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:23:57 <1b>[0mTraining LightCART Regression...<1b>[0m [train]
2026-10-05 11:23:57 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:57 <1b>[0mConverting 1 factor to integer...<1b>[0m [preprocess]
2026-10-05 11:23:57 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightCART<1b>[0m (Decision Tree)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 1.66
<1b>[1m MSE<1b>[22m: 4.24
<1b>[1m RMSE<1b>[22m: 2.06
<1b>[1m R²<1b>[22m: 0.15
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 1.58
<1b>[1m MSE<1b>[22m: 3.75
<1b>[1m RMSE<1b>[22m: 1.94
<1b>[1m R²<1b>[22m: 0.16
2026-10-05 11:23:57 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.35 seconds.<1b>[0m [train]
2026-10-05 11:23:57 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:57 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:23:57 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:57 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:57 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:23:57 <1b>[0mTraining LightCART Regression...<1b>[0m [train]
2026-10-05 11:23:57 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:57 <1b>[0mConverting 1 factor to integer...<1b>[0m [preprocess]
2026-10-05 11:23:57 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightCART<1b>[0m (Decision Tree)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 1.66
<1b>[1m MSE<1b>[22m: 4.24
<1b>[1m RMSE<1b>[22m: 2.06
<1b>[1m R²<1b>[22m: 0.15
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 1.58
<1b>[1m MSE<1b>[22m: 3.75
<1b>[1m RMSE<1b>[22m: 1.94
<1b>[1m R²<1b>[22m: 0.16
2026-10-05 11:23:57 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.35 seconds.<1b>[0m [train]
2026-10-05 11:23:57 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:57 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:23:57 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:57 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:57 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:23:57 <1b>[0mTraining LightCART Classification...<1b>[0m [train]
2026-10-05 11:23:57 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:57 <1b>[0mConverting 2 factors to integer...<1b>[0m [preprocess]
2026-10-05 11:23:58 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightCART<1b>[0m (Decision Tree)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 41 4
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 44
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.911
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.944
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.976
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.917
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.943
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.944
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.211
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.860
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.219
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:23:58 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.27 seconds.<1b>[0m [train]
2026-10-05 11:23:58 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:58 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:23:58 <1b>[0mTraining set: 135 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:58 <1b>[0m Test set: 15 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:58 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:23:58 <1b>[0mTraining LightCART Classification...<1b>[0m [train]
2026-10-05 11:23:58 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:58 <1b>[0mConverting 1 factor to integer...<1b>[0m [preprocess]
2026-10-05 11:23:58 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightCART<1b>[0m (Decision Tree)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 44 1 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 43 2
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 3 42
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.956
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.956
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.978 0.956 0.933
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 0.956 0.978
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.989 0.956 0.956
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 0.915 0.955
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.989 0.977 0.967
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.989 0.935 0.944
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 5 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 5 0
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.933
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.933
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.933
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 1.000 0.800
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 0.900 1.000
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000 0.950 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 0.833 1.000
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 1.000 0.909
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000 0.909 0.889
2026-10-05 11:23:58 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.31 seconds.<1b>[0m [train]
2026-10-05 11:23:58 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:58 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:23:58 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:58 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:58 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:23:58 <1b>[0mTraining LightRF Regression...<1b>[0m [train]
2026-10-05 11:23:58 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:58 <1b>[0mConverting 1 factor to integer...<1b>[0m [preprocess]
2026-10-05 11:23:58 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightRF<1b>[0m (LightGBM Random Forest)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.92
<1b>[1m MSE<1b>[22m: 1.34
<1b>[1m RMSE<1b>[22m: 1.16
<1b>[1m R²<1b>[22m: 0.73
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.89
<1b>[1m MSE<1b>[22m: 1.18
<1b>[1m RMSE<1b>[22m: 1.09
<1b>[1m R²<1b>[22m: 0.73
2026-10-05 11:23:59 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.74 seconds.<1b>[0m [train]
2026-10-05 11:23:59 <1b>[0mConverting 1 factor to integer...<1b>[0m [preprocess]
2026-10-05 11:23:59 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
2026-10-05 11:23:59 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:23:59 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:23:59 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:59 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:23:59 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:23:59 <1b>[0m<> Tuning LightRF by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 11:23:59 <1b>[0m2 parameter combinations x 5 resamples: 10 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 11:23:59 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:23:59 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
\ 8/10 ETA: 1s | Tuning... (10 combinations)
2026-10-05 11:24:02 <1b>[0mBest config to minimize mse:<1b>[0m [tune_GridSearch]
lambda_l1: {0, 0.1} => 0
2026-10-05 11:24:02 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 11:24:02 <1b>[0mTraining LightRF Regression with tuned hyperparameters...<1b>[0m [train]
2026-10-05 11:24:02 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:24:02 <1b>[0mConverting 1 factor to integer...<1b>[0m [preprocess]
2026-10-05 11:24:02 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightRF<1b>[0m (LightGBM Random Forest)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.92
<1b>[1m MSE<1b>[22m: 1.33
<1b>[1m RMSE<1b>[22m: 1.15
<1b>[1m R²<1b>[22m: 0.73
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.88
<1b>[1m MSE<1b>[22m: 1.17
<1b>[1m RMSE<1b>[22m: 1.08
<1b>[1m R²<1b>[22m: 0.74
2026-10-05 11:24:02 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 3.18 seconds.<1b>[0m [train]
2026-10-05 11:24:02 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:24:02 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:24:02 <1b>[0mTraining set: 400 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:24:02 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:24:02 <1b>[0m<> Training LightRF Regression using 3 independent folds...<1b>[0m [train]
2026-10-05 11:24:02 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:24:10 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Regression Model>
LightRF (LightGBM Random Forest)
⚙ Tuned using exhaustive grid search.
⟳ Tested using 3 independent folds.
<Resampled Regression Training Metrics>
Showing mean (sd) across resamples.
MAE: 0.962 (0.025)
MSE: 1.446 (0.126)
RMSE: 1.202 (0.053)
R²: 0.705 (0.030)
<Resampled Regression Test Metrics>
Showing mean (sd) across resamples.
MAE: 1.035 (0.067)
MSE: 1.657 (0.266)
RMSE: 1.284 (0.103)
R²: 0.661 (0.064)
2026-10-05 11:24:10 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 8.03 seconds.<1b>[0m [train]
2026-10-05 11:24:10 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:24:10 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:24:10 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:24:10 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:24:10 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:24:10 <1b>[0mTraining LightRF Classification...<1b>[0m [train]
2026-10-05 11:24:10 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:24:10 <1b>[0mConverting 2 factors to integer...<1b>[0m [preprocess]
2026-10-05 11:24:10 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightRF<1b>[0m (LightGBM Random Forest)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 41 4
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 44
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.911
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.944
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.976
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.917
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.943
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.944
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.996
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.048
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.092
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:24:10 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.19 seconds.<1b>[0m [train]
2026-10-05 11:24:10 <1b>[0mConverting 1 factor to integer...<1b>[0m [preprocess]
2026-10-05 11:24:10 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
2026-10-05 11:24:10 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:24:10 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:24:10 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:24:10 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:24:10 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:24:10 <1b>[0m<> Tuning LightRF by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 11:24:10 <1b>[0m2 parameter combinations x 5 resamples: 10 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 11:24:10 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:24:10 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 11:24:10 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
2026-10-05 11:24:12 <1b>[0mBest config to maximize balanced_accuracy:<1b>[0m [tune_GridSearch]
max_depth: {-1, 5} => -1
2026-10-05 11:24:12 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 11:24:12 <1b>[0mTraining LightRF Classification with tuned hyperparameters...<1b>[0m [train]
2026-10-05 11:24:12 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:24:12 <1b>[0mConverting 2 factors to integer...<1b>[0m [preprocess]
2026-10-05 11:24:12 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightRF<1b>[0m (LightGBM Random Forest)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 41 4
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 44
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.911
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.944
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.976
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.917
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.943
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.944
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.996
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.048
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.092
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:24:12 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 1.62 seconds.<1b>[0m [train]
2026-10-05 11:24:12 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:24:12 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:24:12 <1b>[0mTraining set: 100 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:24:12 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:24:12 <1b>[0m<> Training LightRF Classification using 3 independent folds...<1b>[0m [train]
2026-10-05 11:24:12 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:24:12 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 11:24:13 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Classification Model>
LightRF (LightGBM Random Forest)
⟳ Tested using 3 independent folds.
<Resampled Classification Training Metrics>
Aggregate Confusion Matrix across resamples.
Predicted
Reference virginica versicolor
virginica 95 5
versicolor 4 96
Showing mean (sd) across resamples.
Sensitivity: 0.950 (0.018)
Specificity: 0.960 (0.045)
Balanced Accuracy: 0.955 (0.014)
Ppv: 0.962 (0.042)
Npv: 0.951 (0.015)
F1: 0.955 (0.013)
Accuracy: 0.955 (0.014)
Auc: 0.988 (0.013)
Brier Score: 0.111 (0.022)
<Resampled Classification Test Metrics>
Aggregate Confusion Matrix across resamples.
Predicted
Reference virginica versicolor
virginica 45 5
versicolor 3 47
Showing mean (sd) across resamples.
Sensitivity: 0.901 (0.067)
Specificity: 0.941 (0.102)
Balanced Accuracy: 0.921 (0.044)
Ppv: 0.947 (0.091)
Npv: 0.908 (0.051)
F1: 0.920 (0.042)
Accuracy: 0.921 (0.044)
Auc: 0.976 (0.030)
Brier Score: 0.115 (0.038)
2026-10-05 11:24:13 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.51 seconds.<1b>[0m [train]
2026-10-05 11:24:13 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:24:13 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:24:13 <1b>[0mTraining set: 135 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 11:24:13 <1b>[0m Test set: 15 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 11:24:13 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:24:13 <1b>[0mTraining LightRF Classification...<1b>[0m [train]
2026-10-05 11:24:13 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:24:13 <1b>[0mConverting 1 factor to integer...<1b>[0m [preprocess]
2026-10-05 11:24:13 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightRF<1b>[0m (LightGBM Random Forest)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 45 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 42 2
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 3 42
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.956
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.955
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.956
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 0.933 0.933
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.989 0.967 0.978
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.994 0.950 0.956
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.978 0.933 0.955
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 0.967 0.967
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.989 0.933 0.944
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 5 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 5 0
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.933
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.933
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.933
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 1.000 0.800
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 0.900 1.000
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000 0.950 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 0.833 1.000
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 1.000 0.909
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000 0.909 0.889
2026-10-05 11:24:13 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.45 seconds.<1b>[0m [train]
2026-10-05 11:24:13 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:24:13 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:24:13 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:24:13 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:24:13 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:24:13 <1b>[0mTraining LightGBM Regression...<1b>[0m [train]
2026-10-05 11:24:13 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:24:13 <1b>[0mConverting 1 factor to integer...<1b>[0m [preprocess]
2026-10-05 11:24:13 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightGBM<1b>[0m (Gradient Boosting)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 1.29
<1b>[1m MSE<1b>[22m: 2.63
<1b>[1m RMSE<1b>[22m: 1.62
<1b>[1m R²<1b>[22m: 0.47
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 1.20
<1b>[1m MSE<1b>[22m: 2.33
<1b>[1m RMSE<1b>[22m: 1.53
<1b>[1m R²<1b>[22m: 0.48
2026-10-05 11:24:13 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.27 seconds.<1b>[0m [train]
2026-10-05 11:24:13 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:24:13 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:24:13 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:24:13 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:24:13 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:24:13 <1b>[0m<> Tuning LightGBM by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 11:24:13 <1b>[0m1 parameter combination x 5 resamples: 5 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 11:24:13 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:24:13 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
2026-10-05 11:24:15 <1b>[0mBest config to minimize mse:<1b>[0m [tune_GridSearch]
nrounds: {} => 434
2026-10-05 11:24:15 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 11:24:15 <1b>[0mTraining LightGBM Regression with tuned hyperparameters...<1b>[0m [train]
2026-10-05 11:24:15 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:24:15 <1b>[0mConverting 1 factor to integer...<1b>[0m [preprocess]
2026-10-05 11:24:15 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightGBM<1b>[0m (Gradient Boosting)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.60
<1b>[1m MSE<1b>[22m: 0.58
<1b>[1m RMSE<1b>[22m: 0.76
<1b>[1m R²<1b>[22m: 0.88
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.80
<1b>[1m MSE<1b>[22m: 1.19
<1b>[1m RMSE<1b>[22m: 1.09
<1b>[1m R²<1b>[22m: 0.73
2026-10-05 11:24:20 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 6.72 seconds.<1b>[0m [train]
2026-10-05 11:24:20 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:24:20 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:24:20 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:24:20 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:24:20 <1b>[0m<> Training LightGBM Regression using 3 independent folds...<1b>[0m [train]
2026-10-05 11:24:20 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:24:25 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Regression Model>
LightGBM (Gradient Boosting)
⚙ Tuned using exhaustive grid search.
⟳ Tested using 3 independent folds.
<Resampled Regression Training Metrics>
Showing mean (sd) across resamples.
MAE: 1.287 (0.027)
MSE: 2.609 (0.113)
RMSE: 1.615 (0.035)
R²: 0.473 (0.006)
<Resampled Regression Test Metrics>
Showing mean (sd) across resamples.
MAE: 1.336 (0.047)
MSE: 2.820 (0.253)
RMSE: 1.678 (0.075)
R²: 0.429 (0.020)
2026-10-05 11:24:25 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 5.29 seconds.<1b>[0m [train]
2026-10-05 11:24:25 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:24:25 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:24:25 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:24:25 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:24:25 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:24:25 <1b>[0m<> Tuning LightGBM by exhaustive grid search with 3 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 11:24:25 <1b>[0m1 parameter combination x 3 resamples: 3 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 11:24:25 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:24:25 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 11:24:25 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
2026-10-05 11:24:27 <1b>[0mBest config to maximize balanced_accuracy:<1b>[0m [tune_GridSearch]
nrounds: {} => 375
2026-10-05 11:24:27 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 11:24:27 <1b>[0mTraining LightGBM Classification with tuned hyperparameters...<1b>[0m [train]
2026-10-05 11:24:27 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:24:27 <1b>[0mConverting 2 factors to integer...<1b>[0m [preprocess]
2026-10-05 11:24:27 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightGBM<1b>[0m (Gradient Boosting)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 45 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 2 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.957
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.999
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.017
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.097
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:24:29 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 3.99 seconds.<1b>[0m [train]
2026-10-05 11:24:29 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:24:29 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:24:29 <1b>[0mTraining set: 135 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 11:24:29 <1b>[0m Test set: 15 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 11:24:29 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:24:29 <1b>[0mTraining LightGBM Classification...<1b>[0m [train]
2026-10-05 11:24:29 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:24:30 <1b>[0mConverting 1 factor to integer...<1b>[0m [preprocess]
2026-10-05 11:24:30 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightGBM<1b>[0m (Gradient Boosting)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 45 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 43 2
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 3 42
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.963
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.963
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.963
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 0.956 0.933
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 0.967 0.978
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000 0.961 0.956
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 0.935 0.955
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 0.978 0.967
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000 0.945 0.944
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 5 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 5 0
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.933
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.933
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.933
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 1.000 0.800
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 0.900 1.000
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000 0.950 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 0.833 1.000
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 1.000 0.909
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000 0.909 0.889
2026-10-05 11:24:30 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.77 seconds.<1b>[0m [train]
2026-10-05 11:24:30 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:24:30 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:24:30 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:24:30 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:24:30 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:24:30 <1b>[0mTraining LightRuleFit Regression...<1b>[0m [train]
2026-10-05 11:24:30 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:24:30 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:24:30 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:24:30 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:24:30 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:24:30 <1b>[0mTraining LightGBM Regression...<1b>[0m [train]
2026-10-05 11:24:30 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:24:30 <1b>[0mConverting 1 factor to integer...<1b>[0m [preprocess]
2026-10-05 11:24:30 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightGBM<1b>[0m (Gradient Boosting)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.78
<1b>[1m MSE<1b>[22m: 0.96
<1b>[1m RMSE<1b>[22m: 0.98
<1b>[1m R²<1b>[22m: 0.81
2026-10-05 11:24:31 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.63 seconds.<1b>[0m [train]
2026-10-05 11:24:31 <1b>[0mExtracting LightGBM rules... ✔ [extract_rules]
2026-10-05 11:24:31 <1b>[0mExtracted 180 unique rules.<1b>[0m [extract_rules]
2026-10-05 11:24:31 <1b>[0mMatching180rules to358cases... ✔ [match_cases_by_rules]
2026-10-05 11:24:31 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:24:31 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:24:31 <1b>[0mTraining set: 358 cases x 180 features.<1b>[0m [summarize_supervised]
2026-10-05 11:24:31 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:24:31 <1b>[0mTraining GLMNET Regression...<1b>[0m [train]
2026-10-05 11:24:31 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLMNET<1b>[0m (Elastic Net)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.64
<1b>[1m MSE<1b>[22m: 0.65
<1b>[1m RMSE<1b>[22m: 0.80
<1b>[1m R²<1b>[22m: 0.87
2026-10-05 11:24:32 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.19 seconds.<1b>[0m [train]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightRuleFit<1b>[0m (LightGBM RuleFit)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.64
<1b>[1m MSE<1b>[22m: 0.65
<1b>[1m RMSE<1b>[22m: 0.80
<1b>[1m R²<1b>[22m: 0.87
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.82
<1b>[1m MSE<1b>[22m: 1.25
<1b>[1m RMSE<1b>[22m: 1.12
<1b>[1m R²<1b>[22m: 0.72
2026-10-05 11:24:33 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 2.23 seconds.<1b>[0m [train]
2026-10-05 11:24:33 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:24:33 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:24:33 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:24:33 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:24:33 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:24:33 <1b>[0mTraining LightRuleFit Classification...<1b>[0m [train]
2026-10-05 11:24:33 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:24:33 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:24:33 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:24:33 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:24:33 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:24:33 <1b>[0mTraining LightGBM Classification...<1b>[0m [train]
2026-10-05 11:24:33 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:24:33 <1b>[0mConverting 2 factors to integer...<1b>[0m [preprocess]
2026-10-05 11:24:33 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightGBM<1b>[0m (Gradient Boosting)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 45 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 2 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.957
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.998
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.019
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:24:33 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.33 seconds.<1b>[0m [train]
2026-10-05 11:24:33 <1b>[0mExtracting LightGBM rules... ✔ [extract_rules]
2026-10-05 11:24:33 <1b>[0mExtracted 12 unique rules.<1b>[0m [extract_rules]
2026-10-05 11:24:33 <1b>[0mMatching12rules to90cases... ✔ [match_cases_by_rules]
2026-10-05 11:24:33 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:24:33 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:24:33 <1b>[0mTraining set: 90 cases x 12 features.<1b>[0m [summarize_supervised]
2026-10-05 11:24:33 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:24:33 <1b>[0m<> Tuning GLMNET by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 11:24:33 <1b>[0m1 parameter combination x 5 resamples: 5 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 11:24:33 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:24:33 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 11:24:33 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
2026-10-05 11:24:35 <1b>[0mBest config to maximize balanced_accuracy:<1b>[0m [tune_GridSearch]
lambda: {} => 0.0658148865916772
2026-10-05 11:24:35 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 11:24:35 <1b>[0mCalculating case weights using Inverse Frequency Weighting.<1b>[0m [ifw]
2026-10-05 11:24:35 <1b>[0mTraining GLMNET Classification with tuned hyperparameters...<1b>[0m [train]
2026-10-05 11:24:35 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLMNET<1b>[0m (Elastic Net)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 45 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 2 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.957
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.996
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.028
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:24:35 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 1.75 seconds.<1b>[0m [train]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightRuleFit<1b>[0m (LightGBM RuleFit)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 45 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 2 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.957
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.996
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.028
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.094
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:24:35 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 2.47 seconds.<1b>[0m [train]
2026-10-05 11:24:39 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:24:39 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:24:39 <1b>[0mTraining set: 50 cases x 1 features.<1b>[0m [summarize_supervised]
2026-10-05 11:24:39 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:24:39 <1b>[0mTraining Isotonic Regression...<1b>[0m [train]
2026-10-05 11:24:39 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mIsotonic<1b>[0m (Isotonic Regression)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.51
<1b>[1m MSE<1b>[22m: 0.61
<1b>[1m RMSE<1b>[22m: 0.78
<1b>[1m R²<1b>[22m: 0.99
2026-10-05 11:24:39 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.15 seconds.<1b>[0m [train]
2026-10-05 11:24:39 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:24:39 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:24:39 <1b>[0mTraining set: 200 cases x 1 features.<1b>[0m [summarize_supervised]
2026-10-05 11:24:39 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:24:39 <1b>[0mTraining Isotonic Classification...<1b>[0m [train]
2026-10-05 11:24:39 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mIsotonic<1b>[0m (Isotonic Regression)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mb <1b>[0m<1b>[1;38;2;108;163;160ma <1b>[0m
<1b>[1;38;2;108;163;160m b<1b>[0m 90 6
<1b>[1;38;2;108;163;160m a<1b>[0m 12 92
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.938
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.885
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.911
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.882
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.939
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.910
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.057
Positive Class <1b>[1;38;2;108;163;160mb<1b>[0m
2026-10-05 11:24:39 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.21 seconds.<1b>[0m [train]
2026-10-05 11:24:39 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:24:39 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:24:39 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:24:39 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:24:39 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:24:39 <1b>[0mTraining Ranger Regression...<1b>[0m [train]
2026-10-05 11:24:39 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mRanger<1b>[0m (Random Forest)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.41
<1b>[1m MSE<1b>[22m: 0.27
<1b>[1m RMSE<1b>[22m: 0.52
<1b>[1m R²<1b>[22m: 0.95
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.84
<1b>[1m MSE<1b>[22m: 1.20
<1b>[1m RMSE<1b>[22m: 1.09
<1b>[1m R²<1b>[22m: 0.73
2026-10-05 11:24:40 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.27 seconds.<1b>[0m [train]
2026-10-05 11:24:40 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:24:40 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:24:40 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:24:40 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:24:40 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:24:40 <1b>[0m<> Tuning Ranger by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 11:24:40 <1b>[0m2 parameter combinations x 5 resamples: 10 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 11:24:40 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:24:40 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
2026-10-05 11:24:42 <1b>[0mBest config to minimize mse:<1b>[0m [tune_GridSearch]
mtry: {3, 6} => 3
2026-10-05 11:24:42 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 11:24:42 <1b>[0mTraining Ranger Regression with tuned hyperparameters...<1b>[0m [train]
2026-10-05 11:24:42 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mRanger<1b>[0m (Random Forest)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.37
<1b>[1m MSE<1b>[22m: 0.23
<1b>[1m RMSE<1b>[22m: 0.48
<1b>[1m R²<1b>[22m: 0.95
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.81
<1b>[1m MSE<1b>[22m: 1.15
<1b>[1m RMSE<1b>[22m: 1.07
<1b>[1m R²<1b>[22m: 0.74
2026-10-05 11:24:43 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 2.93 seconds.<1b>[0m [train]
2026-10-05 11:24:43 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:24:43 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:24:43 <1b>[0mTraining set: 400 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:24:43 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:24:43 <1b>[0m<> Training Ranger Regression using 3 independent folds...<1b>[0m [train]
2026-10-05 11:24:43 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
\ 1/3 ETA: 5s | Training outer resamples...
2026-10-05 11:24:53 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Regression Model>
Ranger (Random Forest)
⟳ Tested using 3 independent folds.
<Resampled Regression Training Metrics>
Showing mean (sd) across resamples.
MAE: 0.412 (4.5e-03)
MSE: 0.265 (0.007)
RMSE: 0.514 (0.007)
R²: 0.946 (2.1e-03)
<Resampled Regression Test Metrics>
Showing mean (sd) across resamples.
MAE: 0.880 (3e-03)
MSE: 1.189 (4e-03)
RMSE: 1.090 (1.8e-03)
R²: 0.758 (0.007)
2026-10-05 11:24:53 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 10.47 seconds.<1b>[0m [train]
| 2/3 ETA: 5s | Training outer resamples...
2026-10-05 11:24:53 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:24:53 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:24:53 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:24:53 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:24:53 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:24:53 <1b>[0mTraining Ranger Classification...<1b>[0m [train]
2026-10-05 11:24:53 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mRanger<1b>[0m (Random Forest)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 42 3
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 44
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.933
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.977
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.936
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.955
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.998
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.026
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.045
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:24:53 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.17 seconds.<1b>[0m [train]
2026-10-05 11:24:53 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:24:53 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:24:53 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:24:53 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:24:53 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:24:53 <1b>[0m<> Tuning Ranger by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 11:24:53 <1b>[0m2 parameter combinations x 5 resamples: 10 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 11:24:53 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:24:53 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 11:24:53 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
2026-10-05 11:24:55 <1b>[0mBest config to maximize balanced_accuracy:<1b>[0m [tune_GridSearch]
mtry: {2, 4} => 4
2026-10-05 11:24:55 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 11:24:55 <1b>[0mTraining Ranger Classification with tuned hyperparameters...<1b>[0m [train]
2026-10-05 11:24:55 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mRanger<1b>[0m (Random Forest)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 44 1
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 2 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.967
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.957
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.977
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.967
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.967
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.996
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.022
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.078
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:24:55 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 1.50 seconds.<1b>[0m [train]
2026-10-05 11:24:55 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:24:55 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:24:55 <1b>[0mTraining set: 100 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:24:55 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:24:55 <1b>[0m<> Training Ranger Classification using 3 independent folds...<1b>[0m [train]
2026-10-05 11:24:55 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:24:55 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 11:24:55 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Classification Model>
Ranger (Random Forest)
⟳ Tested using 3 independent folds.
<Resampled Classification Training Metrics>
Aggregate Confusion Matrix across resamples.
Predicted
Reference virginica versicolor
virginica 98 2
versicolor 4 96
Showing mean (sd) across resamples.
Sensitivity: 0.980 (0.017)
Specificity: 0.960 (0.045)
Balanced Accuracy: 0.970 (0.014)
Ppv: 0.963 (0.041)
Npv: 0.980 (0.017)
F1: 0.971 (0.013)
Accuracy: 0.970 (0.014)
Auc: 0.998 (1.8e-03)
Brier Score: 0.020 (0.008)
<Resampled Classification Test Metrics>
Aggregate Confusion Matrix across resamples.
Predicted
Reference virginica versicolor
virginica 46 4
versicolor 3 47
Showing mean (sd) across resamples.
Sensitivity: 0.920 (0.033)
Specificity: 0.940 (2.1e-03)
Balanced Accuracy: 0.930 (0.016)
Ppv: 0.939 (2.1e-03)
Npv: 0.923 (0.029)
F1: 0.929 (0.018)
Accuracy: 0.930 (0.016)
Auc: 0.986 (0.006)
Brier Score: 0.054 (0.016)
2026-10-05 11:24:55 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.53 seconds.<1b>[0m [train]
2026-10-05 11:24:55 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:24:55 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:24:55 <1b>[0mTraining set: 135 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 11:24:55 <1b>[0m Test set: 15 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 11:24:55 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:24:55 <1b>[0mTraining Ranger Classification...<1b>[0m [train]
2026-10-05 11:24:55 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mRanger<1b>[0m (Random Forest)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 45 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 44 1
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 2 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.978
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 0.978 0.956
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 0.978 0.989
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000 0.978 0.972
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 0.957 0.977
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 0.989 0.978
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000 0.967 0.966
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 5 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 5 0
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 0 5
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 1.000
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000 1.000 1.000
2026-10-05 11:24:56 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.21 seconds.<1b>[0m [train]
2026-10-05 11:24:56 <1b>[0m<> Calibrating LightRF classification...<1b>[0m [calibrate]
2026-10-05 11:24:56 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:24:56 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:24:56 <1b>[0mTraining set: 90 cases x 1 features.<1b>[0m [summarize_supervised]
2026-10-05 11:24:56 <1b>[0m Test set: 10 cases x 1 features.<1b>[0m [summarize_supervised]
2026-10-05 11:24:56 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:24:56 <1b>[0mTraining Isotonic Classification...<1b>[0m [train]
2026-10-05 11:24:56 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mIsotonic<1b>[0m (Isotonic Regression)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 45 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 2 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.957
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.997
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.017
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.100
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:24:56 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.16 seconds.<1b>[0m [train]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightRF<1b>[0m (LightGBM Random Forest)
<1b>[1;38;2;115;100;242m⟋<1b>[0m Calibrated using Isotonic Regression.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics (Pre => Post Calibration)<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 41 => 45 4 => 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 => 2 44 => 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.91 => 1.00
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.98 => 0.96
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.94 => 0.98
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.98 => 0.96
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.92 => 1.00
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.94 => 0.98
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.94 => 0.98
<1b>[22;38;2;128;128;128m Auc<1b>[0m 1.00 => 1.00
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.05 => 0.02
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics (Pre => Post Calibration)<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 => 5 0 => 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 => 1 4 => 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.00 => 1.00
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.80 => 0.80
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.90 => 0.90
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.83 => 0.83
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.00 => 1.00
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.91 => 0.91
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.90 => 0.90
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.90 => 0.90
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.09 => 0.10
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:24:56 <1b>[0m</> Calibration done.<1b>[0m [calibrate]
2026-10-05 11:24:56 <1b>[0mConverting 1 factor to integer...<1b>[0m [preprocess]
2026-10-05 11:24:56 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
2026-10-05 11:24:56 <1b>[0m<> Calibrating LightRF resampled classification...<1b>[0m [calibrate]
<Resampled Classification Model>
LightRF (LightGBM Random Forest)
⟳ Tested using 3 independent folds.
⟋ Calibrated using Isotonic Regression with 5 independent folds.
<Resampled Classification Training Metrics (Pre => Post Calibration)>
Showing mean (sd) across resamples, Pre => Post calibration.
sensitivity: 0.95 (0.02) => 1.00 (0.00)
specificity: 0.96 (0.04) => 0.92 (0.08)
balanced_accuracy: 0.96 (0.01) => 0.96 (0.04)
ppv: 0.96 (0.04) => 0.93 (0.07)
npv: 0.95 (0.02) => 1.00 (0.00)
f1: 0.96 (0.01) => 0.96 (0.04)
accuracy: 0.96 (0.01) => 0.96 (0.04)
auc: 0.99 (0.01) => 0.98 (0.02)
brier_score: 0.11 (0.02) => 0.03 (0.03)
<Resampled Classification Test Metrics (Pre => Post Calibration)>
Showing mean (sd) across resamples, Pre => Post calibration.
sensitivity: 0.90 (0.07) => 0.93 (0.19)
specificity: 0.94 (0.10) => 0.85 (0.20)
balanced_accuracy: 0.92 (0.04) => 0.89 (0.12)
ppv: 0.95 (0.09) => 0.89 (0.14)
npv: 0.91 (0.05) => 0.96 (0.12)
f1: 0.92 (0.04) => 0.89 (0.14)
accuracy: 0.92 (0.04) => 0.89 (0.12)
auc: 0.98 (0.03) => 0.92 (0.11)
brier_score: 0.12 (0.04) => 0.09 (0.11)
2026-10-05 11:24:58 <1b>[0m</> Calibration done.<1b>[0m [calibrate]
Generalized Linear Model was used for regression. R-squared was 0.83 on the training set and 0.77 on the test set.
Generalized Linear Model was used for regression. R-squared was 0.83 on the training set and 0.77 on the test set.
Generalized Linear Model was used for classification. Balanced accuracy was 0.98 in the training set and 0.90 in the test set.
Generalized Linear Model was used for regression. R-squared was 0.83 on the training set and 0.77 on the test set.
Generalized Linear Model was used for classification. Balanced accuracy was 0.98 in the training set and 0.90 in the test set.
Generalized Linear Model was used for regression. Mean R-squared was 0.83 on the training set and 0.82 on the test set across 3 independent folds.
Generalized Linear Model was used for classification. Mean balanced accuracy was 0.99 in the training set and 0.89 in the test set across 3 independent folds.
Generalized Linear Model (GLM) and Classification and Regression Trees (CART) were used for Regression.
The top-performing model was GLM with a test-set Rsq of 0.817, followed by CART with rsq of 0.629 respectively.
Generalized Linear Model (GLM) and Classification and Regression Trees (CART) were used for Classification.
The top-performing model was CART with a test-set Balanced Accuracy of 0.920, followed by GLM with balanced_accuracy of 0.888 respectively.
Generalized Linear Model (GLM) and Classification and Regression Trees (CART) were used for Regression.
The top-performing model was GLM with a test-set Rsq of 0.770, followed by CART with rsq of 0.595 respectively.
2026-10-05 11:25:07 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:25:07 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:25:07 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:25:07 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:25:07 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:25:07 <1b>[0mTraining GLM Regression...<1b>[0m [train]
2026-10-05 11:25:07 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLM<1b>[0m (Generalized Linear Model)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.73
<1b>[1m MSE<1b>[22m: 0.82
<1b>[1m RMSE<1b>[22m: 0.91
<1b>[1m R²<1b>[22m: 0.83
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.74
<1b>[1m MSE<1b>[22m: 1.03
<1b>[1m RMSE<1b>[22m: 1.01
<1b>[1m R²<1b>[22m: 0.77
2026-10-05 11:25:07 <1b>[0mWriting data to /tmp/RtmpNpyEX1/working_dir/RtmpjfjDos/file1821263efd31b5/mod_r_glm...✔ 0.2 secs [rt_save]
2026-10-05 11:25:07 <1b>[0mReload with: > obj <- readRDS('/tmp/RtmpNpyEX1/working_dir/RtmpjfjDos/file1821263efd31b5/mod_r_glm/train_GLM.rds')<1b>[0m [rt_save]
2026-10-05 11:25:07 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.30 seconds.<1b>[0m [train]
2026-10-05 11:25:07 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:25:07 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:25:07 <1b>[0mTraining set: 400 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:25:07 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: c(`_R_CHECK_LIMIT_CORES_` = 1) }<1b>[0m [get_n_workers]
2026-10-05 11:25:07 <1b>[0m<> Training GLM Regression using 3 independent folds...<1b>[0m [train]
2026-10-05 11:25:07 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:25:08 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Regression Model>
GLM (Generalized Linear Model)
⟳ Tested using 3 independent folds.
<Resampled Regression Training Metrics>
Showing mean (sd) across resamples.
MAE: 0.723 (0.039)
MSE: 0.831 (0.080)
RMSE: 0.911 (0.044)
R²: 0.830 (0.019)
<Resampled Regression Test Metrics>
Showing mean (sd) across resamples.
MAE: 0.756 (0.096)
MSE: 0.909 (0.188)
RMSE: 0.950 (0.101)
R²: 0.813 (0.046)
2026-10-05 11:25:08 <1b>[0mWriting data to /tmp/RtmpNpyEX1/working_dir/RtmpjfjDos/file182126687804f8/resmod_r_glm...✔ 0.6 secs [rt_save]
2026-10-05 11:25:08 <1b>[0mReload with: > obj <- readRDS('/tmp/RtmpNpyEX1/working_dir/RtmpjfjDos/file182126687804f8/resmod_r_glm/train_GLM.rds')<1b>[0m [rt_save]
2026-10-05 11:25:08 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.89 seconds.<1b>[0m [train]
Classification and Regression Trees (CART), LightGBM Random Forest (LightRF), and Gradient Boosting (LightGBM) were used for Classification.
The top-performing model was CART with a test-set Balanced Accuracy of 0.900, followed by LightRF and LightGBM with balanced_accuracy of 0.900 and 0.900 respectively.
Classification and Regression Trees (CART), LightGBM Random Forest (LightRF), and Gradient Boosting (LightGBM) were used for Classification.
The top-performing model was CART with a test-set Balanced Accuracy of 0.900, followed by LightRF and LightGBM with balanced_accuracy of 0.900 and 0.900 respectively.
Elastic Net (GLMNET), Support Vector Machine with Radial Kernel (RadialSVM), and LightGBM Random Forest (LightRF) were used for Regression.
The top-performing model was RadialSVM with a test-set Rsq of 0.773, followed by GLMNET and LightRF with rsq of 0.772 and 0.735 respectively.
Elastic Net (GLMNET), Support Vector Machine with Radial Kernel (RadialSVM), and LightGBM Random Forest (LightRF) were used for Regression.
The top-performing model was RadialSVM with a test-set Rsq of 0.773, followed by GLMNET and LightRF with rsq of 0.772 and 0.735 respectively.
Generalized Linear Model (GLM), Support Vector Machine with Linear Kernel (LinearSVM), and LightGBM Random Forest (LightRF) were used for Classification.
The top-performing model was LinearSVM with a test-set Balanced Accuracy of 0.960, followed by LightRF and GLM with balanced_accuracy of 0.921 and 0.888 respectively.
Generalized Linear Model (GLM), Support Vector Machine with Linear Kernel (LinearSVM), and LightGBM Random Forest (LightRF) were used for Classification.
The top-performing model was LinearSVM with a test-set Balanced Accuracy of 0.960, followed by LightRF and GLM with balanced_accuracy of 0.921 and 0.888 respectively.
Generalized Linear Model (GLM), Support Vector Machine with Linear Kernel (LinearSVM), and LightGBM Random Forest (LightRF) were used for Regression.
The top-performing model was LinearSVM with a test-set Rsq of 0.821, followed by GLM and LightRF with rsq of 0.817 and 0.661 respectively.
Generalized Linear Model (GLM), Support Vector Machine with Linear Kernel (LinearSVM), and LightGBM Random Forest (LightRF) were used for Regression.
The top-performing model was LinearSVM with a test-set Rsq of 0.821, followed by GLM and LightRF with rsq of 0.817 and 0.661 respectively.
2026-10-05 11:25:09 <1b>[0m<> Calibrating GLM resampled classification...<1b>[0m [calibrate]
<Resampled Classification Model>
GLM (Generalized Linear Model)
⟳ Tested using 3 independent folds.
⟋ Calibrated using Isotonic Regression with 5 independent folds.
<Resampled Classification Training Metrics (Pre => Post Calibration)>
Showing mean (sd) across resamples, Pre => Post calibration.
sensitivity: 0.99 (0.02) => 0.95 (0.08)
specificity: 0.99 (0.02) => 0.91 (0.09)
balanced_accuracy: 0.99 (0.02) => 0.93 (0.07)
ppv: 0.99 (0.02) => 0.92 (0.08)
npv: 0.99 (0.02) => 0.95 (0.07)
f1: 0.99 (0.02) => 0.93 (0.07)
accuracy: 0.99 (0.02) => 0.93 (0.07)
auc: 1.00 (3.7e-03) => 0.94 (0.05)
brier_score: 0.01 (0.01) => 0.05 (0.05)
<Resampled Classification Test Metrics (Pre => Post Calibration)>
Showing mean (sd) across resamples, Pre => Post calibration.
sensitivity: 0.88 (0.13) => 0.94 (0.13)
specificity: 0.90 (0.09) => 0.89 (0.20)
balanced_accuracy: 0.89 (0.09) => 0.91 (0.12)
ppv: 0.90 (0.10) => 0.92 (0.15)
npv: 0.89 (0.12) => 0.94 (0.12)
f1: 0.89 (0.10) => 0.92 (0.11)
accuracy: 0.89 (0.09) => 0.91 (0.12)
auc: 0.94 (0.06) => 0.92 (0.12)
brier_score: 0.10 (0.09) => 0.08 (0.11)
2026-10-05 11:25:12 <1b>[0m</> Calibration done.<1b>[0m [calibrate]
2026-10-05 11:25:12 <1b>[0m<> Calibrating CART resampled classification...<1b>[0m [calibrate]
<Resampled Classification Model>
CART (Classification and Regression Trees)
⚙ Tuned using exhaustive grid search.
⟳ Tested using 3 independent folds.
⟋ Calibrated using Isotonic Regression with 5 independent folds.
<Resampled Classification Training Metrics (Pre => Post Calibration)>
Showing mean (sd) across resamples, Pre => Post calibration.
sensitivity: 0.94 (0.06) => 0.92 (0.08)
specificity: 0.95 (0.06) => 0.92 (0.08)
balanced_accuracy: 0.95 (0.01) => 0.92 (0.02)
ppv: 0.95 (0.06) => 0.93 (0.07)
npv: 0.95 (0.05) => 0.93 (0.07)
f1: 0.94 (0.01) => 0.92 (0.02)
accuracy: 0.95 (0.01) => 0.92 (0.02)
auc: 0.95 (0.01) => 0.92 (0.02)
brier_score: 0.05 (0.01) => 0.07 (0.02)
<Resampled Classification Test Metrics (Pre => Post Calibration)>
Showing mean (sd) across resamples, Pre => Post calibration.
sensitivity: 0.92 (0.09) => 0.92 (0.14)
specificity: 0.92 (0.09) => 0.92 (0.14)
balanced_accuracy: 0.92 (0.01) => 0.92 (0.08)
ppv: 0.93 (0.08) => 0.94 (0.10)
npv: 0.93 (0.08) => 0.94 (0.11)
f1: 0.92 (0.02) => 0.92 (0.09)
accuracy: 0.92 (0.01) => 0.92 (0.08)
auc: 0.91 (0.03) => 0.92 (0.08)
brier_score: 0.07 (0.01) => 0.08 (0.06)
2026-10-05 11:25:14 <1b>[0m</> Calibration done.<1b>[0m [calibrate]
2026-10-05 11:25:14 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:25:14 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:25:14 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:25:14 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:25:14 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:25:14 <1b>[0mPreprocessing...<1b>[0m [train]
2026-10-05 11:25:14 <1b>[0mTraining GLM Classification...<1b>[0m [train]
2026-10-05 11:25:14 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLM<1b>[0m (Generalized Linear Model)
<1b>[1;38;2;82;101;81m▣<1b>[0m Preprocessed using centering, scaling.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 44 1
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 44
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.978
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.998
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.018
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.093
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:25:14 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.22 seconds.<1b>[0m [train]
rtemis Color System
<1b>[1m highlight_col<1b>[22m: <1b>[22;38;2;108;163;160m░<1b>[0m<1b>[22;38;2;108;163;160m░<1b>[0m<1b>[22;38;2;108;163;160m▓<1b>[0m<1b>[22;38;2;108;163;160m▓<1b>[0m<1b>[22;38;2;108;163;160m█<1b>[0m<1b>[22;38;2;108;163;160m█<1b>[0m<1b>[22;38;2;108;163;160m▓<1b>[0m<1b>[22;38;2;108;163;160m▓<1b>[0m<1b>[22;38;2;108;163;160m░<1b>[0m<1b>[22;38;2;108;163;160m░<1b>[0m
<1b>[1m col_warn<1b>[22m: <1b>[22;38;2;240;137;4m░<1b>[0m<1b>[22;38;2;240;137;4m░<1b>[0m<1b>[22;38;2;240;137;4m▓<1b>[0m<1b>[22;38;2;240;137;4m▓<1b>[0m<1b>[22;38;2;240;137;4m█<1b>[0m<1b>[22;38;2;240;137;4m█<1b>[0m<1b>[22;38;2;240;137;4m▓<1b>[0m<1b>[22;38;2;240;137;4m▓<1b>[0m<1b>[22;38;2;240;137;4m░<1b>[0m<1b>[22;38;2;240;137;4m░<1b>[0m
<1b>[1m col_error<1b>[22m: <1b>[22;38;2;234;56;74m░<1b>[0m<1b>[22;38;2;234;56;74m░<1b>[0m<1b>[22;38;2;234;56;74m▓<1b>[0m<1b>[22;38;2;234;56;74m▓<1b>[0m<1b>[22;38;2;234;56;74m█<1b>[0m<1b>[22;38;2;234;56;74m█<1b>[0m<1b>[22;38;2;234;56;74m▓<1b>[0m<1b>[22;38;2;234;56;74m▓<1b>[0m<1b>[22;38;2;234;56;74m░<1b>[0m<1b>[22;38;2;234;56;74m░<1b>[0m
<1b>[1m col_success<1b>[22m: <1b>[22;38;2;0;204;143m░<1b>[0m<1b>[22;38;2;0;204;143m░<1b>[0m<1b>[22;38;2;0;204;143m▓<1b>[0m<1b>[22;38;2;0;204;143m▓<1b>[0m<1b>[22;38;2;0;204;143m█<1b>[0m<1b>[22;38;2;0;204;143m█<1b>[0m<1b>[22;38;2;0;204;143m▓<1b>[0m<1b>[22;38;2;0;204;143m▓<1b>[0m<1b>[22;38;2;0;204;143m░<1b>[0m<1b>[22;38;2;0;204;143m░<1b>[0m
<1b>[1m col_preprocessor<1b>[22m: <1b>[22;38;2;82;101;81m░<1b>[0m<1b>[22;38;2;82;101;81m░<1b>[0m<1b>[22;38;2;82;101;81m▓<1b>[0m<1b>[22;38;2;82;101;81m▓<1b>[0m<1b>[22;38;2;82;101;81m█<1b>[0m<1b>[22;38;2;82;101;81m█<1b>[0m<1b>[22;38;2;82;101;81m▓<1b>[0m<1b>[22;38;2;82;101;81m▓<1b>[0m<1b>[22;38;2;82;101;81m░<1b>[0m<1b>[22;38;2;82;101;81m░<1b>[0m
<1b>[1m col_decom<1b>[22m: <1b>[22;38;2;15;106;102m░<1b>[0m<1b>[22;38;2;15;106;102m░<1b>[0m<1b>[22;38;2;15;106;102m▓<1b>[0m<1b>[22;38;2;15;106;102m▓<1b>[0m<1b>[22;38;2;15;106;102m█<1b>[0m<1b>[22;38;2;15;106;102m█<1b>[0m<1b>[22;38;2;15;106;102m▓<1b>[0m<1b>[22;38;2;15;106;102m▓<1b>[0m<1b>[22;38;2;15;106;102m░<1b>[0m<1b>[22;38;2;15;106;102m░<1b>[0m
<1b>[1m col_outer<1b>[22m: <1b>[22;38;2;190;46;95m░<1b>[0m<1b>[22;38;2;190;46;95m░<1b>[0m<1b>[22;38;2;190;46;95m▓<1b>[0m<1b>[22;38;2;190;46;95m▓<1b>[0m<1b>[22;38;2;190;46;95m█<1b>[0m<1b>[22;38;2;190;46;95m█<1b>[0m<1b>[22;38;2;190;46;95m▓<1b>[0m<1b>[22;38;2;190;46;95m▓<1b>[0m<1b>[22;38;2;190;46;95m░<1b>[0m<1b>[22;38;2;190;46;95m░<1b>[0m
<1b>[1m col_tuner<1b>[22m: <1b>[22;38;2;243;132;255m░<1b>[0m<1b>[22;38;2;243;132;255m░<1b>[0m<1b>[22;38;2;243;132;255m▓<1b>[0m<1b>[22;38;2;243;132;255m▓<1b>[0m<1b>[22;38;2;243;132;255m█<1b>[0m<1b>[22;38;2;243;132;255m█<1b>[0m<1b>[22;38;2;243;132;255m▓<1b>[0m<1b>[22;38;2;243;132;255m▓<1b>[0m<1b>[22;38;2;243;132;255m░<1b>[0m<1b>[22;38;2;243;132;255m░<1b>[0m
<1b>[1m col_info<1b>[22m: <1b>[22;38;2;70;109;150m░<1b>[0m<1b>[22;38;2;70;109;150m░<1b>[0m<1b>[22;38;2;70;109;150m▓<1b>[0m<1b>[22;38;2;70;109;150m▓<1b>[0m<1b>[22;38;2;70;109;150m█<1b>[0m<1b>[22;38;2;70;109;150m█<1b>[0m<1b>[22;38;2;70;109;150m▓<1b>[0m<1b>[22;38;2;70;109;150m▓<1b>[0m<1b>[22;38;2;70;109;150m░<1b>[0m<1b>[22;38;2;70;109;150m░<1b>[0m
warning: Failed to inspect Python interpreter from search path at `/usr/sbin/pypy`
cause: Can't use Python at `/usr/sbin/pypy`
cause: Python executable does not support `-I` flag. Please use Python 3.6 or newer.
Group counts:
group
Low NS High
1 98 1
2026-10-05 11:25:29 <1b>[0m▶<1b>[0m [massGLM]
2026-10-05 11:25:29 <1b>[0mScaling and centering 40 numeric features...<1b>[0m [preprocess]
2026-10-05 11:25:29 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
2026-10-05 11:25:29 <1b>[0mFitting 40 GLMs of family gaussian with 2 predictors each...<1b>[0m [massGLM]
2026-10-05 11:25:29 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.28 seconds.<1b>[0m [massGLM]
2026-10-05 11:25:29 <1b>[0mPlotting coefficients for x1 x 40 outcomes.<1b>[0m [`plot.rtemis::MassGLM`]
Group counts:
group
Low NS High
1 37 2
2026-10-05 11:25:29 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:25:29 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:25:29 <1b>[0mTraining set: 100 cases x 3 features.<1b>[0m [summarize_supervised]
2026-10-05 11:25:29 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:25:29 <1b>[0mTraining GLM Regression...<1b>[0m [train]
2026-10-05 11:25:29 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLM<1b>[0m (Generalized Linear Model)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.84
<1b>[1m MSE<1b>[22m: 1.04
<1b>[1m RMSE<1b>[22m: 1.02
<1b>[1m R²<1b>[22m: 0.69
2026-10-05 11:25:29 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.11 seconds.<1b>[0m [train]
2026-10-05 11:25:30 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:25:30 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:25:30 <1b>[0mTraining set: 100 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 11:25:30 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:25:30 <1b>[0mTraining GLM Classification...<1b>[0m [train]
2026-10-05 11:25:30 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLM<1b>[0m (Generalized Linear Model)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 49 1
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 49
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.980
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.980
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.980
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.980
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.980
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.980
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.980
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.997
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.019
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:25:30 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.18 seconds.<1b>[0m [train]
2026-10-05 11:25:30 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:25:30 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:25:30 <1b>[0mTraining set: 100 cases x 3 features.<1b>[0m [summarize_supervised]
2026-10-05 11:25:30 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: c(`_R_CHECK_LIMIT_CORES_` = 1) }<1b>[0m [get_n_workers]
2026-10-05 11:25:30 <1b>[0m<> Training GLM Regression using 3 independent folds...<1b>[0m [train]
2026-10-05 11:25:30 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:25:30 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Regression Model>
GLM (Generalized Linear Model)
⟳ Tested using 3 independent folds.
<Resampled Regression Training Metrics>
Showing mean (sd) across resamples.
MAE: 0.821 (0.012)
MSE: 1.008 (0.053)
RMSE: 1.004 (0.026)
R²: 0.697 (0.020)
<Resampled Regression Test Metrics>
Showing mean (sd) across resamples.
MAE: 0.901 (0.029)
MSE: 1.193 (0.074)
RMSE: 1.092 (0.034)
R²: 0.637 (0.047)
2026-10-05 11:25:30 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.40 seconds.<1b>[0m [train]
[ FAIL 1 | WARN 4 | SKIP 4 | PASS 356 ]
══ Skipped tests (4) ═══════════════════════════════════════════════════════════
• For local testing only; requires CSV file (3): 'test_ClusterConfig.R:19:3',
'test_DecomposeConfig.R:19:3', 'test_SuperConfig.R:48:3'
• empty test (1):
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test_Clustering.R:96:3'): cluster_DBSCAN() succeeds ─────────────────
Error: approx must be a single, finite, nonnegative number.
Backtrace:
▆
1. └─rtemis::cluster(...) at test_Clustering.R:96:3
2. └─rtemis:::cluster_(config = config, x = x, verbosity = verbosity)
3. ├─S7::S7_dispatch()
4. └─rtemis (local) `method(cluster_, rtemis::DBSCANConfig)`(...)
5. └─dbscan::dbscan(...)
6. └─dbscan:::.validate_nonnegative_scalar(extra$approx %||% 0, "approx")
[ FAIL 1 | WARN 4 | SKIP 4 | PASS 356 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 1.2.7
Check: tests
Result: ERROR
Running ‘testthat.R’ [89s/397s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(rtemis)
.:rtemis 1.2.7 🌊 x86_64-pc-linux-gnu
> library(testthat)
Attaching package: 'testthat'
The following object is masked from 'package:rtemis':
describe
>
> test_check("rtemis")
Attaching package: 'data.table'
The following object is masked from 'package:base':
%notin%
2026-10-05 10:58:34 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 10:58:34 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 10:58:34 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 10:58:34 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 10:58:34 <1b>[0mUsing max n bins possible = 3.<1b>[0m [kfold]
2026-10-05 10:58:35 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Created file: /tmp/RtmppWvuXM/working_dir/RtmpBt0OZL/rtemis_cluster.json<1b>[0m [write_lines]
2026-10-05 10:58:35 ✖ rtemis_range_error<1b>[0m [setup_KMeans]
2026-10-05 10:58:35 <1b>[0m▶<1b>[0m [cluster]
2026-10-05 10:58:35 <1b>[0mInput: 150 cases x 4 features.<1b>[0m [summarize_unsupervised]
2026-10-05 10:58:35 <1b>[0mClustering with KMeans...<1b>[0m [cluster]
2026-10-05 10:58:35 <1b>[0mChecking unsupervised data... ✔ [check_unsupervised_data]
2026-10-05 10:58:35 <1b>[0mClustering with KMeans ...<1b>[0m [cluster_]
2026-10-05 10:58:35 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.34 seconds.<1b>[0m [cluster]
2026-10-05 10:58:35 <1b>[0m▶<1b>[0m [cluster]
2026-10-05 10:58:35 <1b>[0mInput: 150 cases x 4 features.<1b>[0m [summarize_unsupervised]
2026-10-05 10:58:35 <1b>[0mClustering with KMeans...<1b>[0m [cluster]
2026-10-05 10:58:35 <1b>[0mChecking unsupervised data... ✔ [check_unsupervised_data]
2026-10-05 10:58:35 <1b>[0mClustering with KMeans ...<1b>[0m [cluster_]
2026-10-05 10:58:36 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.22 seconds.<1b>[0m [cluster]
2026-10-05 10:58:36 <1b>[0m▶<1b>[0m [cluster]
2026-10-05 10:58:36 <1b>[0mInput: 150 cases x 4 features.<1b>[0m [summarize_unsupervised]
2026-10-05 10:58:36 <1b>[0mClustering with HardCL...<1b>[0m [cluster]
2026-10-05 10:58:36 <1b>[0mChecking unsupervised data... ✔ [check_unsupervised_data]
2026-10-05 10:58:36 <1b>[0mClustering with HardCL ...<1b>[0m [cluster_]
2026-10-05 10:58:36 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.10 seconds.<1b>[0m [cluster]
2026-10-05 10:58:37 <1b>[0m▶<1b>[0m [cluster]
2026-10-05 10:58:37 <1b>[0mInput: 150 cases x 4 features.<1b>[0m [summarize_unsupervised]
2026-10-05 10:58:37 <1b>[0mClustering with NeuralGas...<1b>[0m [cluster]
2026-10-05 10:58:37 <1b>[0mChecking unsupervised data... ✔ [check_unsupervised_data]
2026-10-05 10:58:37 <1b>[0mClustering with NeuralGas ...<1b>[0m [cluster_]
2026-10-05 10:58:37 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.21 seconds.<1b>[0m [cluster]
2026-10-05 10:58:37 <1b>[0m▶<1b>[0m [cluster]
2026-10-05 10:58:37 <1b>[0mInput: 150 cases x 4 features.<1b>[0m [summarize_unsupervised]
2026-10-05 10:58:37 <1b>[0mClustering with CMeans...<1b>[0m [cluster]
2026-10-05 10:58:37 <1b>[0mChecking unsupervised data... ✔ [check_unsupervised_data]
2026-10-05 10:58:37 <1b>[0mClustering with CMeans ...<1b>[0m [cluster_]
Iteration: 1, Error: 1.0222276897
Iteration: 2, Error: 0.4426354825
Iteration: 3, Error: 0.4040115623
Iteration: 4, Error: 0.4035611242
Iteration: 5, Error: 0.4034484182
Iteration: 6, Error: 0.4034031102
Iteration: 7, Error: 0.4033845090
Iteration: 8, Error: 0.4033768332
Iteration: 9, Error: 0.4033736566
Iteration: 10, Error: 0.4033723396
Iteration: 11, Error: 0.4033717929
Iteration: 12, Error: 0.4033715658
Iteration: 13, Error: 0.4033714714
Iteration: 14, Error: 0.4033714321
Iteration: 15, Error: 0.4033714158
Iteration: 16, Error: 0.4033714090
Iteration: 17 converged, Error: 0.4033714062
2026-10-05 10:58:37 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.05 seconds.<1b>[0m [cluster]
2026-10-05 10:58:38 <1b>[0m▶<1b>[0m [cluster]
2026-10-05 10:58:38 <1b>[0mInput: 150 cases x 4 features.<1b>[0m [summarize_unsupervised]
2026-10-05 10:58:38 <1b>[0mClustering with DBSCAN...<1b>[0m [cluster]
2026-10-05 10:58:38 <1b>[0mChecking unsupervised data... ✔ [check_unsupervised_data]
2026-10-05 10:58:38 <1b>[0mClustering with DBSCAN ...<1b>[0m [cluster_]
Saving _problems/test_Clustering-100.R
2026-10-05 10:58:39 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Created file: /tmp/RtmppWvuXM/working_dir/RtmpBt0OZL/rtemis_decompose.json<1b>[0m [write_lines]
2026-10-05 10:58:39 <1b>[0m▶<1b>[0m [decomp]
2026-10-05 10:58:39 <1b>[0mInput: 150 cases x 4 features.<1b>[0m [summarize_unsupervised]
2026-10-05 10:58:39 <1b>[0mDecomposing with PCA...<1b>[0m [decomp]
2026-10-05 10:58:39 <1b>[0mChecking unsupervised data... ✔ [check_unsupervised_data]
2026-10-05 10:58:39 <1b>[0mDecomposing with PCA ...<1b>[0m [decomp_]
2026-10-05 10:58:39 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.02 seconds.<1b>[0m [decomp]
2026-10-05 10:58:39 <1b>[0m▶<1b>[0m [decomp]
2026-10-05 10:58:39 <1b>[0mInput: 150 cases x 4 features.<1b>[0m [summarize_unsupervised]
2026-10-05 10:58:39 <1b>[0mDecomposing with ICA...<1b>[0m [decomp]
2026-10-05 10:58:39 <1b>[0mChecking unsupervised data... ✔ [check_unsupervised_data]
2026-10-05 10:58:39 <1b>[0mDecomposing with ICA ...<1b>[0m [decomp_]
Centering
colstandard
Whitening
Symmetric FastICA using logcosh approx. to neg-entropy function
Iteration 1 tol=0.038537
Iteration 2 tol=0.004207
Iteration 3 tol=0.002599
Iteration 4 tol=0.001640
Iteration 5 tol=0.000951
Iteration 6 tol=0.000483
Iteration 7 tol=0.000217
Iteration 8 tol=0.000088
2026-10-05 10:58:39 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.01 seconds.<1b>[0m [decomp]
2026-10-05 10:58:45 <1b>[0m▶<1b>[0m [decomp]
2026-10-05 10:58:45 <1b>[0mInput: 150 cases x 4 features.<1b>[0m [summarize_unsupervised]
2026-10-05 10:58:45 <1b>[0mDecomposing with NMF...<1b>[0m [decomp]
2026-10-05 10:58:45 <1b>[0mChecking unsupervised data... ✔ [check_unsupervised_data]
2026-10-05 10:58:45 <1b>[0mDecomposing with NMF ...<1b>[0m [decomp_]
2026-10-05 10:58:58 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 12.62 seconds.<1b>[0m [decomp]
2026-10-05 10:58:58 <1b>[0m▶<1b>[0m [decomp]
2026-10-05 10:58:58 <1b>[0mInput: 150 cases x 4 features.<1b>[0m [summarize_unsupervised]
2026-10-05 10:58:58 <1b>[0mDecomposing with UMAP...<1b>[0m [decomp]
2026-10-05 10:58:58 <1b>[0mChecking unsupervised data... ✔ [check_unsupervised_data]
2026-10-05 10:58:58 <1b>[0mDecomposing with UMAP ...<1b>[0m [decomp_]
2026-10-05 10:59:07 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 9.31 seconds.<1b>[0m [decomp]
2026-10-05 10:59:07 <1b>[0m▶<1b>[0m [decomp]
2026-10-05 10:59:07 <1b>[0mInput: 150 cases x 4 features.<1b>[0m [summarize_unsupervised]
2026-10-05 10:59:07 <1b>[0mDecomposing with UMAP...<1b>[0m [decomp]
2026-10-05 10:59:07 <1b>[0mChecking unsupervised data... ✔ [check_unsupervised_data]
2026-10-05 10:59:07 <1b>[0mDecomposing with UMAP ...<1b>[0m [decomp_]
2026-10-05 10:59:14 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 6.52 seconds.<1b>[0m [decomp]
2026-10-05 10:59:14 <1b>[0m▶<1b>[0m [decomp]
2026-10-05 10:59:14 <1b>[0mInput: 150 cases x 4 features.<1b>[0m [summarize_unsupervised]
2026-10-05 10:59:14 <1b>[0mDecomposing with tSNE...<1b>[0m [decomp]
2026-10-05 10:59:14 <1b>[0mChecking unsupervised data... ✔ [check_unsupervised_data]
2026-10-05 10:59:14 <1b>[0mDecomposing with tSNE ...<1b>[0m [decomp_]
2026-10-05 10:59:14 <1b>[0mRemoving 1 duplicate case...<1b>[0m [preprocess]
2026-10-05 10:59:14 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
2026-10-05 10:59:14 <1b>[0m▶<1b>[0m [decomp]
2026-10-05 10:59:14 <1b>[0mInput: 149 cases x 4 features.<1b>[0m [summarize_unsupervised]
2026-10-05 10:59:14 <1b>[0mDecomposing with tSNE...<1b>[0m [decomp]
2026-10-05 10:59:14 <1b>[0mChecking unsupervised data... ✔ [check_unsupervised_data]
2026-10-05 10:59:14 <1b>[0mDecomposing with tSNE ...<1b>[0m [decomp_]
2026-10-05 10:59:16 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 1.71 seconds.<1b>[0m [decomp]
2026-10-05 10:59:16 <1b>[0m▶<1b>[0m [decomp]
2026-10-05 10:59:16 <1b>[0mInput: 150 cases x 4 features.<1b>[0m [summarize_unsupervised]
2026-10-05 10:59:16 <1b>[0mDecomposing with Isomap...<1b>[0m [decomp]
2026-10-05 10:59:16 <1b>[0mChecking unsupervised data... ✔ [check_unsupervised_data]
2026-10-05 10:59:16 <1b>[0mDecomposing with Isomap ...<1b>[0m [decomp_]
2026-10-05 10:59:17 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.20 seconds.<1b>[0m [decomp]
2026-10-05 10:59:19 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 10:59:19 <1b>[0mUsing max n bins possible = 3.<1b>[0m [kfold]
2026-10-05 10:59:20 <1b>[0mScaling and centering 4 numeric features...<1b>[0m [preprocess]
2026-10-05 10:59:20 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
2026-10-05 10:59:20 <1b>[0mScaling and centering 4 numeric features...<1b>[0m [preprocess]
2026-10-05 10:59:20 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
2026-10-05 10:59:21 <1b>[0mScaling and centering 4 numeric features...<1b>[0m [preprocess]
2026-10-05 10:59:21 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
2026-10-05 10:59:21 <1b>[0mApplying preprocessing to test data...<1b>[0m [preprocess]
2026-10-05 10:59:21 <1b>[0mScaling and centering 4 numeric features...<1b>[0m [preprocess]
2026-10-05 10:59:21 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
2026-10-05 10:59:21 <1b>[0mImputing missing values using get_mode (discrete) and mean (continuous)...<1b>[0m [preprocess]
2026-10-05 10:59:21 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
2026-10-05 10:59:21 <1b>[0mOne hot encoding g... ✔ [one_hot]
2026-10-05 10:59:21 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
2026-10-05 10:59:22 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 10:59:22 <1b>[0mUsing max n bins possible = 3.<1b>[0m [kfold]
2026-10-05 10:59:22 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 10:59:22 <1b>[0mUsing max n bins possible = 3.<1b>[0m [kfold]
2026-10-05 10:59:22 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Created file: /tmp/RtmppWvuXM/working_dir/RtmpBt0OZL/rtemis_super.json<1b>[0m [write_lines]
2026-10-05 10:59:22 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Created file: /tmp/RtmppWvuXM/working_dir/RtmpBt0OZL/rtemis_decom.json<1b>[0m [write_lines]
2026-10-05 10:59:23 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Created file: /tmp/RtmppWvuXM/working_dir/RtmpBt0OZL/rtemis_clust.json<1b>[0m [write_lines]
2026-10-05 10:59:23 ✖ rtemis_value_error<1b>[0m [.detect_config_kind]
2026-10-05 10:59:23 ✖ rtemis_value_error<1b>[0m [.detect_config_kind]
2026-10-05 10:59:24 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 10:59:24 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 10:59:24 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 10:59:24 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 10:59:24 <1b>[0mUsing max n bins possible = 3.<1b>[0m [kfold]
2026-10-05 10:59:24 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 10:59:24 <1b>[0m▶<1b>[0m [train]
2026-10-05 10:59:24 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 10:59:24 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 10:59:24 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 10:59:24 <1b>[0mTraining GLM Regression...<1b>[0m [train]
2026-10-05 10:59:24 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLM<1b>[0m (Generalized Linear Model)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.73
<1b>[1m MSE<1b>[22m: 0.82
<1b>[1m RMSE<1b>[22m: 0.91
<1b>[1m R²<1b>[22m: 0.83
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.74
<1b>[1m MSE<1b>[22m: 1.03
<1b>[1m RMSE<1b>[22m: 1.01
<1b>[1m R²<1b>[22m: 0.77
2026-10-05 10:59:25 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.43 seconds.<1b>[0m [train]
2026-10-05 10:59:25 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 10:59:25 <1b>[0m▶<1b>[0m [train]
2026-10-05 10:59:25 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 10:59:25 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 10:59:25 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 10:59:25 <1b>[0mTraining GLM Regression...<1b>[0m [train]
2026-10-05 10:59:25 <1b>[0mChecking data is ready for training...
2026-10-05 10:59:25 ✖ rtemis_missing_data<1b>[0m [check_supervised]
2026-10-05 10:59:25 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 10:59:25 <1b>[0m▶<1b>[0m [train]
2026-10-05 10:59:25 <1b>[0mTraining set: 400 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 10:59:25 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: c(`_R_CHECK_LIMIT_CORES_` = 1) }<1b>[0m [get_n_workers]
2026-10-05 10:59:25 <1b>[0m<> Training GLM Regression using 3 independent folds...<1b>[0m [train]
2026-10-05 10:59:25 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 10:59:26 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Regression Model>
GLM (Generalized Linear Model)
⟳ Tested using 3 independent folds.
<Resampled Regression Training Metrics>
Showing mean (sd) across resamples.
MAE: 0.723 (0.010)
MSE: 0.833 (0.031)
RMSE: 0.913 (0.017)
R²: 0.830 (0.009)
<Resampled Regression Test Metrics>
Showing mean (sd) across resamples.
MAE: 0.747 (0.023)
MSE: 0.892 (0.065)
RMSE: 0.944 (0.034)
R²: 0.817 (0.017)
2026-10-05 10:59:26 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 1.13 seconds.<1b>[0m [train]
2026-10-05 10:59:26 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 10:59:26 <1b>[0m▶<1b>[0m [train]
2026-10-05 10:59:26 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 10:59:26 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 10:59:26 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 10:59:26 <1b>[0mTraining GLM Classification...<1b>[0m [train]
2026-10-05 10:59:26 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLM<1b>[0m (Generalized Linear Model)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 44 1
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 44
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.978
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.998
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.018
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.093
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 10:59:27 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.36 seconds.<1b>[0m [train]
2026-10-05 10:59:27 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 10:59:27 <1b>[0m▶<1b>[0m [train]
2026-10-05 10:59:27 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 10:59:27 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 10:59:27 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 10:59:27 <1b>[0mCalculating case weights using Inverse Frequency Weighting.<1b>[0m [ifw]
2026-10-05 10:59:27 <1b>[0mTraining GLM Classification...<1b>[0m [train]
2026-10-05 10:59:27 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLM<1b>[0m (Generalized Linear Model)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 44 1
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 44
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.978
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.998
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.018
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.093
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 10:59:27 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.18 seconds.<1b>[0m [train]
2026-10-05 10:59:27 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 10:59:27 <1b>[0m▶<1b>[0m [train]
2026-10-05 10:59:27 <1b>[0mTraining set: 100 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 10:59:27 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: c(`_R_CHECK_LIMIT_CORES_` = 1) }<1b>[0m [get_n_workers]
2026-10-05 10:59:27 <1b>[0m<> Training GLM Classification using 3 independent folds...<1b>[0m [train]
2026-10-05 10:59:27 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 10:59:27 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 10:59:28 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Classification Model>
GLM (Generalized Linear Model)
⟳ Tested using 3 independent folds.
<Resampled Classification Training Metrics>
Aggregate Confusion Matrix across resamples.
Predicted
Reference virginica versicolor
virginica 99 1
versicolor 1 99
Showing mean (sd) across resamples.
Sensitivity: 0.990 (0.017)
Specificity: 0.990 (0.017)
Balanced Accuracy: 0.990 (0.017)
Ppv: 0.990 (0.017)
Npv: 0.990 (0.017)
F1: 0.990 (0.017)
Accuracy: 0.990 (0.017)
Auc: 0.998 (3.7e-03)
Brier Score: 0.007 (0.012)
<Resampled Classification Test Metrics>
Aggregate Confusion Matrix across resamples.
Predicted
Reference virginica versicolor
virginica 44 6
versicolor 5 45
Showing mean (sd) across resamples.
Sensitivity: 0.877 (0.125)
Specificity: 0.898 (0.095)
Balanced Accuracy: 0.888 (0.092)
Ppv: 0.898 (0.100)
Npv: 0.886 (0.118)
F1: 0.885 (0.096)
Accuracy: 0.888 (0.092)
Auc: 0.939 (0.062)
Brier Score: 0.101 (0.088)
2026-10-05 10:59:28 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 1.07 seconds.<1b>[0m [train]
2026-10-05 10:59:28 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 10:59:28 <1b>[0m▶<1b>[0m [train]
2026-10-05 10:59:28 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 10:59:28 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 10:59:28 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 10:59:28 <1b>[0mTraining GLMNET Regression...<1b>[0m [train]
2026-10-05 10:59:29 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLMNET<1b>[0m (Elastic Net)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.73
<1b>[1m MSE<1b>[22m: 0.82
<1b>[1m RMSE<1b>[22m: 0.91
<1b>[1m R²<1b>[22m: 0.83
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.74
<1b>[1m MSE<1b>[22m: 1.02
<1b>[1m RMSE<1b>[22m: 1.01
<1b>[1m R²<1b>[22m: 0.77
2026-10-05 10:59:29 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.45 seconds.<1b>[0m [train]
2026-10-05 10:59:29 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 10:59:29 <1b>[0m▶<1b>[0m [train]
2026-10-05 10:59:29 <1b>[0m▶ train GLMNET Regression<1b>[0m [session_render]
2026-10-05 10:59:29 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 10:59:29 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 10:59:29 <1b>[0m// Max workers: 2 { Algorithm: 1; Tuning: 2; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 10:59:29 <1b>[0m ▶ tune<1b>[0m [session_render]
2026-10-05 10:59:29 <1b>[0m▶<1b>[0m [tune_GridSearch]
2026-10-05 10:59:29 <1b>[0m<> Tuning GLMNET by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 10:59:29 <1b>[0m1 parameter combination x 5 resamples: 5 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 10:59:29 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mKFoldConfig<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m n<1b>[22m: 5
<1b>[1mstratify_var<1b>[22m: NULL
<1b>[1mstrat_n_bins<1b>[22m: 4
<1b>[1m id_strat<1b>[22m: NULL
<1b>[1m seed<1b>[22m: NULL
2026-10-05 10:59:31 <1b>[0mTuning using future (mirai_multisession); N workers: 2<1b>[0m [tune_GridSearch]
2026-10-05 10:59:31 ℹ Current future plan:<1b>[0m [tune_GridSearch]
mirai_multisession:
- args: function (..., workers = 2L, envir = parent.frame())
- tweaked: TRUE
- call: future::plan(strategy = requested_plan, workers = n_workers)
MiraiMultisessionFutureBackend:
Inherits: MiraiFutureBackend, MultiprocessFutureBackend, FutureBackend
UUID: 230ccc32f863e0d1d8e628a5074d342b
Number of workers: 2
Number of free workers: 2
Available cores: 2
Automatic garbage collection: FALSE
Early signaling: FALSE
Interrupts are enabled: TRUE
Maximum total size of globals: +Inf
Maximum total size of value: +Inf
Number of active futures: 0
Number of futures since start: 0 (0 created, 0 launched, 0 finished)
Total runtime of futures: 0 secs (NaN secs/finished future)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLMNET<1b>[0m (Elastic Net)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.77
<1b>[1m MSE<1b>[22m: 0.94
<1b>[1m RMSE<1b>[22m: 0.97
<1b>[1m R²<1b>[22m: 0.81
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mValidation Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.78
<1b>[1m MSE<1b>[22m: 0.91
<1b>[1m RMSE<1b>[22m: 0.96
<1b>[1m R²<1b>[22m: 0.83
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLMNET<1b>[0m (Elastic Net)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.75
<1b>[1m MSE<1b>[22m: 0.90
<1b>[1m RMSE<1b>[22m: 0.95
<1b>[1m R²<1b>[22m: 0.82
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mValidation Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.75
<1b>[1m MSE<1b>[22m: 0.87
<1b>[1m RMSE<1b>[22m: 0.93
<1b>[1m R²<1b>[22m: 0.80
2026-10-05 10:59:35 ℹ Running grid line #1/5...<1b>[0m [tune_GridSearch]
2026-10-05 10:59:35 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 10:59:36 <1b>[0m▶<1b>[0m [train]
2026-10-05 10:59:36 <1b>[0m Training set: 285 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 10:59:36 <1b>[0mValidation set: 73 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 10:59:36 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 10:59:36 <1b>[0mTraining GLMNET Regression...<1b>[0m [train]
2026-10-05 10:59:39 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 10:59:40 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 4.78 seconds.<1b>[0m [train]
2026-10-05 10:59:40 ℹ Running grid line #2/5...<1b>[0m [tune_GridSearch]
2026-10-05 10:59:40 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 10:59:40 <1b>[0m▶<1b>[0m [train]
2026-10-05 10:59:41 <1b>[0m Training set: 288 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 10:59:41 <1b>[0mValidation set: 70 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 10:59:41 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 10:59:41 <1b>[0mTraining GLMNET Regression...<1b>[0m [train]
2026-10-05 10:59:41 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 10:59:41 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.85 seconds.<1b>[0m [train]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLMNET<1b>[0m (Elastic Net)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.78
<1b>[1m MSE<1b>[22m: 0.93
<1b>[1m RMSE<1b>[22m: 0.97
<1b>[1m R²<1b>[22m: 0.81
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mValidation Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.68
<1b>[1m MSE<1b>[22m: 0.80
<1b>[1m RMSE<1b>[22m: 0.89
<1b>[1m R²<1b>[22m: 0.83
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLMNET<1b>[0m (Elastic Net)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.73
<1b>[1m MSE<1b>[22m: 0.88
<1b>[1m RMSE<1b>[22m: 0.94
<1b>[1m R²<1b>[22m: 0.82
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mValidation Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.79
<1b>[1m MSE<1b>[22m: 0.85
<1b>[1m RMSE<1b>[22m: 0.92
<1b>[1m R²<1b>[22m: 0.83
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLMNET<1b>[0m (Elastic Net)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.73
<1b>[1m MSE<1b>[22m: 0.82
<1b>[1m RMSE<1b>[22m: 0.91
<1b>[1m R²<1b>[22m: 0.83
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mValidation Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.82
<1b>[1m MSE<1b>[22m: 1.14
<1b>[1m RMSE<1b>[22m: 1.07
<1b>[1m R²<1b>[22m: 0.79
2026-10-05 10:59:36 ℹ Running grid line #3/5...<1b>[0m [tune_GridSearch]
2026-10-05 10:59:36 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 10:59:36 <1b>[0m▶<1b>[0m [train]
2026-10-05 10:59:36 <1b>[0m Training set: 286 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 10:59:36 <1b>[0mValidation set: 72 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 10:59:36 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 10:59:36 <1b>[0mTraining GLMNET Regression...<1b>[0m [train]
2026-10-05 10:59:41 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 10:59:42 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 5.80 seconds.<1b>[0m [train]
2026-10-05 10:59:42 ℹ Running grid line #4/5...<1b>[0m [tune_GridSearch]
2026-10-05 10:59:42 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 10:59:42 <1b>[0m▶<1b>[0m [train]
2026-10-05 10:59:42 <1b>[0m Training set: 288 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 10:59:42 <1b>[0mValidation set: 70 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 10:59:42 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 10:59:42 <1b>[0mTraining GLMNET Regression...<1b>[0m [train]
2026-10-05 10:59:42 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 10:59:42 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.16 seconds.<1b>[0m [train]
2026-10-05 10:59:42 ℹ Running grid line #5/5...<1b>[0m [tune_GridSearch]
2026-10-05 10:59:42 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 10:59:42 <1b>[0m▶<1b>[0m [train]
2026-10-05 10:59:42 <1b>[0m Training set: 285 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 10:59:42 <1b>[0mValidation set: 73 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 10:59:42 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 10:59:42 <1b>[0mTraining GLMNET Regression...<1b>[0m [train]
2026-10-05 10:59:42 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 10:59:42 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.24 seconds.<1b>[0m [train]
2026-10-05 10:59:42 ℹ Extracting best lambda from GLMNET models...<1b>[0m [tune_GridSearch]
2026-10-05 10:59:43 <1b>[0mBest config to minimize mse:<1b>[0m [tune_GridSearch]
lambda: {} => 0.140014948415186
2026-10-05 10:59:43 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 13.20 seconds.<1b>[0m [tune_GridSearch]
2026-10-05 10:59:43 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 10:59:43 <1b>[0m ✔ tune (13.5 s)<1b>[0m [session_render]
2026-10-05 10:59:43 <1b>[0mTraining GLMNET Regression with tuned hyperparameters...<1b>[0m [train]
2026-10-05 10:59:43 <1b>[0m ▶ train_alg GLMNET<1b>[0m [session_render]
2026-10-05 10:59:43 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 10:59:43 ℹ NCOL(xm): 6<1b>[0m [train_]
2026-10-05 10:59:43 ℹ Updated hyperparameters[["penalty_factor"]] to all 1s.<1b>[0m [train_]
2026-10-05 10:59:43 <1b>[0m ✔ train_alg GLMNET (239 ms)<1b>[0m [session_render]
2026-10-05 10:59:43 <1b>[0m ▶ predict<1b>[0m [session_render]
2026-10-05 10:59:43 <1b>[0m ✔ predict (31 ms)<1b>[0m [session_render]
2026-10-05 10:59:43 <1b>[0m ▶ varimp GLMNET<1b>[0m [session_render]
2026-10-05 10:59:43 <1b>[0m ✔ varimp GLMNET (27 ms)<1b>[0m [session_render]
2026-10-05 10:59:43 <1b>[0m ▶ metrics<1b>[0m [session_render]
2026-10-05 10:59:43 <1b>[0m ✔ metrics (157 ms)<1b>[0m [session_render]
2026-10-05 10:59:43 <1b>[0m✔ train GLMNET Regression (14.0 s)<1b>[0m [session_render]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLMNET<1b>[0m (Elastic Net)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.75
<1b>[1m MSE<1b>[22m: 0.90
<1b>[1m RMSE<1b>[22m: 0.95
<1b>[1m R²<1b>[22m: 0.82
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.73
<1b>[1m MSE<1b>[22m: 0.95
<1b>[1m RMSE<1b>[22m: 0.98
<1b>[1m R²<1b>[22m: 0.79
2026-10-05 10:59:43 <1b>[0mModels trained:<1b>[0m [session_report]
2026-10-05 10:59:43 <1b>[0m tuning: 1 combos ⨉ 5 inner resamples = 5<1b>[0m [session_report]
2026-10-05 10:59:43 <1b>[0m + final: 1 per fit = 1<1b>[0m [session_report]
2026-10-05 10:59:43 <1b>[0m total = 6 models<1b>[0m [session_report]
2026-10-05 10:59:43 <1b>[0m 6 succeeded in 14.0 s<1b>[0m [session_report]
2026-10-05 10:59:43 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 14.08 seconds.<1b>[0m [train]
2026-10-05 10:59:43 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 10:59:44 <1b>[0m▶<1b>[0m [train]
2026-10-05 10:59:44 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 10:59:44 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 10:59:44 <1b>[0m// Max workers: 2 { Algorithm: 1; Tuning: 2; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 10:59:44 ✖ rtemis_value_error<1b>[0m [tune_GridSearch]
2026-10-05 10:59:44 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 10:59:44 <1b>[0m▶<1b>[0m [train]
2026-10-05 10:59:44 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 10:59:44 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 10:59:44 <1b>[0m// Max workers: 2 { Algorithm: 1; Tuning: 2; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 10:59:44 <1b>[0m<> Tuning GLMNET by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 10:59:44 <1b>[0m1 parameter combination x 5 resamples: 5 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 10:59:44 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 10:59:44 <1b>[0mTuning using mirai; N workers: 2<1b>[0m [tune_GridSearch]
======>------------------------ 20% | ETA: 35s
==============================> 100% | ETA: 0s
2026-10-05 10:59:54 <1b>[0mBest config to minimize mse:<1b>[0m [tune_GridSearch]
lambda: {} => 0.144320781405745
2026-10-05 10:59:54 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 10:59:55 <1b>[0mTraining GLMNET Regression with tuned hyperparameters...<1b>[0m [train]
2026-10-05 10:59:55 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLMNET<1b>[0m (Elastic Net)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.76
<1b>[1m MSE<1b>[22m: 0.90
<1b>[1m RMSE<1b>[22m: 0.95
<1b>[1m R²<1b>[22m: 0.82
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.73
<1b>[1m MSE<1b>[22m: 0.95
<1b>[1m RMSE<1b>[22m: 0.98
<1b>[1m R²<1b>[22m: 0.79
2026-10-05 10:59:55 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 11.33 seconds.<1b>[0m [train]
2026-10-05 10:59:55 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 10:59:55 <1b>[0m▶<1b>[0m [train]
2026-10-05 10:59:55 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 10:59:55 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 10:59:55 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 10:59:55 <1b>[0m<> Tuning GLMNET by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 10:59:55 <1b>[0m2 parameter combinations x 5 resamples: 10 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 10:59:55 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 10:59:55 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
\ 6/10 ETA: 2s | Tuning... (10 combinations)
2026-10-05 11:00:01 <1b>[0mBest config to minimize mse:<1b>[0m [tune_GridSearch]
alpha: {0, 1} => 1
lambda: {} => 0.134770240620737
2026-10-05 11:00:01 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 11:00:01 <1b>[0mTraining GLMNET Regression with tuned hyperparameters...<1b>[0m [train]
2026-10-05 11:00:01 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLMNET<1b>[0m (Elastic Net)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.75
<1b>[1m MSE<1b>[22m: 0.89
<1b>[1m RMSE<1b>[22m: 0.95
<1b>[1m R²<1b>[22m: 0.82
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.73
<1b>[1m MSE<1b>[22m: 0.96
<1b>[1m RMSE<1b>[22m: 0.98
<1b>[1m R²<1b>[22m: 0.79
2026-10-05 11:00:01 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 6.24 seconds.<1b>[0m [train]
2026-10-05 11:00:01 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:00:01 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:00:01 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:00:01 <1b>[0mTuning parallelization enabled.<1b>[0m [get_n_workers]
2026-10-05 11:00:01 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:00:01 <1b>[0m<> Training GLMNET Regression using 3 independent folds...<1b>[0m [train]
2026-10-05 11:00:01 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
\ 4/10 ETA: 4s | Tuning... (10 combinations)
| 9/10 ETA: 1s | Tuning... (10 combinations)
\ 4/10 ETA: 3s | Tuning... (10 combinations)
| 8/10 ETA: 2s | Tuning... (10 combinations)
\ 8/10 ETA: 1s | Tuning... (10 combinations)
2026-10-05 11:00:21 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Regression Model>
GLMNET (Elastic Net)
⚙ Tuned using exhaustive grid search.
⟳ Tested using 3 independent folds.
<Resampled Regression Training Metrics>
Showing mean (sd) across resamples.
MAE: 0.760 (0.030)
MSE: 0.910 (0.042)
RMSE: 0.954 (0.022)
R²: 0.816 (0.011)
<Resampled Regression Test Metrics>
Showing mean (sd) across resamples.
MAE: 0.766 (0.056)
MSE: 0.921 (0.077)
RMSE: 0.959 (0.040)
R²: 0.813 (0.021)
2026-10-05 11:00:22 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 20.21 seconds.<1b>[0m [train]
2026-10-05 11:00:22 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:00:22 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:00:22 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:00:22 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:00:22 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:00:22 <1b>[0mCalculating case weights using Inverse Frequency Weighting.<1b>[0m [ifw]
2026-10-05 11:00:22 <1b>[0mTraining GLMNET Classification...<1b>[0m [train]
2026-10-05 11:00:22 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLMNET<1b>[0m (Elastic Net)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 43 2
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 2 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.956
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.998
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.024
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.055
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:00:22 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.31 seconds.<1b>[0m [train]
2026-10-05 11:00:22 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:00:22 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:00:22 <1b>[0mTraining set: 135 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 11:00:22 <1b>[0m Test set: 15 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 11:00:22 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:00:22 <1b>[0m<> Tuning GLMNET by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 11:00:22 <1b>[0m1 parameter combination x 5 resamples: 5 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 11:00:22 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:00:22 <1b>[0mUsing max n bins possible = 3.<1b>[0m [kfold]
2026-10-05 11:00:22 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
\ 1/5 ETA: 9s | Tuning... (5 combinations)
/ 4/5 ETA: 3s | Tuning... (5 combinations)
2026-10-05 11:00:34 <1b>[0mBest config to maximize balanced_accuracy:<1b>[0m [tune_GridSearch]
lambda: {} => 0.00821962912592355
2026-10-05 11:00:34 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 11:00:34 <1b>[0mCalculating case weights using Inverse Frequency Weighting.<1b>[0m [ifw]
2026-10-05 11:00:34 <1b>[0mTraining GLMNET Classification with tuned hyperparameters...<1b>[0m [train]
2026-10-05 11:00:34 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLMNET<1b>[0m (Elastic Net)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 45 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 42 3
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 2 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.963
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.963
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.963
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 0.933 0.956
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 0.978 0.967
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000 0.956 0.961
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 0.955 0.935
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 0.967 0.978
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000 0.944 0.945
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 5 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 5 0
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.933
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.933
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.933
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 1.000 0.800
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 0.900 1.000
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000 0.950 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 0.833 1.000
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 1.000 0.909
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000 0.909 0.889
2026-10-05 11:00:35 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 12.57 seconds.<1b>[0m [train]
2026-10-05 11:00:35 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:00:35 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:00:35 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:00:35 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:00:35 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:00:35 <1b>[0mTraining GAM Regression...<1b>[0m [train]
2026-10-05 11:00:35 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGAM<1b>[0m (Generalized Additive Model)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.72
<1b>[1m MSE<1b>[22m: 0.80
<1b>[1m RMSE<1b>[22m: 0.89
<1b>[1m R²<1b>[22m: 0.84
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.75
<1b>[1m MSE<1b>[22m: 1.01
<1b>[1m RMSE<1b>[22m: 1.00
<1b>[1m R²<1b>[22m: 0.77
2026-10-05 11:00:36 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.88 seconds.<1b>[0m [train]
2026-10-05 11:00:36 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:00:36 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:00:36 <1b>[0mTraining set: 358 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:00:36 <1b>[0m Test set: 42 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:00:36 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:00:36 <1b>[0mTraining GAM Regression...<1b>[0m [train]
2026-10-05 11:00:36 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGAM<1b>[0m (Generalized Additive Model)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 1.49
<1b>[1m MSE<1b>[22m: 2.95
<1b>[1m RMSE<1b>[22m: 1.72
<1b>[1m R²<1b>[22m: 0.40
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 1.26
<1b>[1m MSE<1b>[22m: 2.20
<1b>[1m RMSE<1b>[22m: 1.48
<1b>[1m R²<1b>[22m: 0.51
2026-10-05 11:00:37 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.95 seconds.<1b>[0m [train]
2026-10-05 11:00:37 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:00:37 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:00:37 <1b>[0mTraining set: 358 cases x 1 features.<1b>[0m [summarize_supervised]
2026-10-05 11:00:37 <1b>[0m Test set: 42 cases x 1 features.<1b>[0m [summarize_supervised]
2026-10-05 11:00:37 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:00:37 <1b>[0mTraining GAM Regression...<1b>[0m [train]
2026-10-05 11:00:37 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGAM<1b>[0m (Generalized Additive Model)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 1.37
<1b>[1m MSE<1b>[22m: 2.80
<1b>[1m RMSE<1b>[22m: 1.67
<1b>[1m R²<1b>[22m: 0.43
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 1.16
<1b>[1m MSE<1b>[22m: 1.93
<1b>[1m RMSE<1b>[22m: 1.39
<1b>[1m R²<1b>[22m: 0.57
2026-10-05 11:00:37 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.41 seconds.<1b>[0m [train]
2026-10-05 11:00:37 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:00:37 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:00:37 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:00:37 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:00:37 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: c(`_R_CHECK_LIMIT_CORES_` = 1); Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:00:37 <1b>[0m<> Tuning GAM by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 11:00:37 <1b>[0m3 parameter combinations x 5 resamples: 15 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 11:00:37 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:00:37 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
\ 7/15 ETA: 4s | Tuning... (15 combinations)
/ 14/15 ETA: 1s | Tuning... (15 combinations)
2026-10-05 11:00:47 <1b>[0mBest config to minimize mse:<1b>[0m [tune_GridSearch]
k: {3, 5, 7} => 3
2026-10-05 11:00:47 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 11:00:47 <1b>[0mTraining GAM Regression with tuned hyperparameters...<1b>[0m [train]
2026-10-05 11:00:47 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGAM<1b>[0m (Generalized Additive Model)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.72
<1b>[1m MSE<1b>[22m: 0.82
<1b>[1m RMSE<1b>[22m: 0.91
<1b>[1m R²<1b>[22m: 0.83
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.74
<1b>[1m MSE<1b>[22m: 1.02
<1b>[1m RMSE<1b>[22m: 1.01
<1b>[1m R²<1b>[22m: 0.77
2026-10-05 11:00:48 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 10.44 seconds.<1b>[0m [train]
2026-10-05 11:00:48 ✖ rtemis_dim_error<1b>[0m [predict_supervised_]
2026-10-05 11:00:48 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:00:48 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:00:48 <1b>[0mTraining set: 400 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:00:48 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: c(`_R_CHECK_LIMIT_CORES_` = 1) }<1b>[0m [get_n_workers]
2026-10-05 11:00:48 <1b>[0m<> Training GAM Regression using 3 independent folds...<1b>[0m [train]
2026-10-05 11:00:48 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:00:49 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Regression Model>
GAM (Generalized Additive Model)
⟳ Tested using 3 independent folds.
<Resampled Regression Training Metrics>
Showing mean (sd) across resamples.
MAE: 0.709 (0.025)
MSE: 0.796 (0.067)
RMSE: 0.891 (0.038)
R²: 0.838 (0.012)
<Resampled Regression Test Metrics>
Showing mean (sd) across resamples.
MAE: 0.759 (0.059)
MSE: 0.908 (0.136)
RMSE: 0.951 (0.070)
R²: 0.815 (0.024)
2026-10-05 11:00:49 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.99 seconds.<1b>[0m [train]
2026-10-05 11:00:49 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:00:49 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:00:49 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:00:49 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:00:49 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:00:49 <1b>[0mTraining GAM Classification...<1b>[0m [train]
2026-10-05 11:00:49 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGAM<1b>[0m (Generalized Additive Model)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 45 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 45
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Auc<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 2.2e-06
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.100
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:00:49 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.17 seconds.<1b>[0m [train]
2026-10-05 11:00:49 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:00:49 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:00:49 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:00:49 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:00:49 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:00:49 <1b>[0mCalculating case weights using Inverse Frequency Weighting.<1b>[0m [ifw]
2026-10-05 11:00:49 <1b>[0mTraining GAM Classification...<1b>[0m [train]
2026-10-05 11:00:49 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGAM<1b>[0m (Generalized Additive Model)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 45 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 45
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Auc<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 2.2e-06
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.100
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:00:50 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.59 seconds.<1b>[0m [train]
2026-10-05 11:00:50 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:00:50 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:00:50 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:00:50 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:00:50 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:00:50 <1b>[0mTraining LinearSVM Regression...<1b>[0m [train]
2026-10-05 11:00:50 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:00:50 <1b>[0mOne hot encoding g... ✔ [one_hot]
2026-10-05 11:00:50 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLinearSVM<1b>[0m (Support Vector Machine with Linear Kernel)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.72
<1b>[1m MSE<1b>[22m: 0.83
<1b>[1m RMSE<1b>[22m: 0.91
<1b>[1m R²<1b>[22m: 0.83
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.75
<1b>[1m MSE<1b>[22m: 1.02
<1b>[1m RMSE<1b>[22m: 1.01
<1b>[1m R²<1b>[22m: 0.77
2026-10-05 11:00:50 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.64 seconds.<1b>[0m [train]
2026-10-05 11:00:50 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:00:50 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:00:50 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:00:50 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:00:50 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:00:50 <1b>[0m<> Tuning LinearSVM by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 11:00:50 <1b>[0m2 parameter combinations x 5 resamples: 10 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 11:00:50 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:00:50 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
\ 8/10 ETA: 1s | Tuning... (10 combinations)
2026-10-05 11:00:59 <1b>[0mBest config to minimize mse:<1b>[0m [tune_GridSearch]
cost: {1, 10} => 1
2026-10-05 11:00:59 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 11:00:59 <1b>[0mTraining LinearSVM Regression with tuned hyperparameters...<1b>[0m [train]
2026-10-05 11:00:59 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:00:59 <1b>[0mOne hot encoding g... ✔ [one_hot]
2026-10-05 11:00:59 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLinearSVM<1b>[0m (Support Vector Machine with Linear Kernel)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.72
<1b>[1m MSE<1b>[22m: 0.83
<1b>[1m RMSE<1b>[22m: 0.91
<1b>[1m R²<1b>[22m: 0.83
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.75
<1b>[1m MSE<1b>[22m: 1.02
<1b>[1m RMSE<1b>[22m: 1.01
<1b>[1m R²<1b>[22m: 0.77
2026-10-05 11:00:59 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 8.93 seconds.<1b>[0m [train]
2026-10-05 11:00:59 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:00:59 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:00:59 <1b>[0mTraining set: 400 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:00:59 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: c(`_R_CHECK_LIMIT_CORES_` = 1) }<1b>[0m [get_n_workers]
2026-10-05 11:00:59 <1b>[0m<> Training LinearSVM Regression using 3 independent folds...<1b>[0m [train]
2026-10-05 11:00:59 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:01:00 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Regression Model>
LinearSVM (Support Vector Machine with Linear Kernel)
⟳ Tested using 3 independent folds.
<Resampled Regression Training Metrics>
Showing mean (sd) across resamples.
MAE: 0.723 (0.021)
MSE: 0.845 (0.022)
RMSE: 0.919 (0.012)
R²: 0.827 (0.008)
<Resampled Regression Test Metrics>
Showing mean (sd) across resamples.
MAE: 0.741 (0.043)
MSE: 0.875 (0.056)
RMSE: 0.935 (0.030)
R²: 0.821 (0.019)
2026-10-05 11:01:01 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 1.40 seconds.<1b>[0m [train]
2026-10-05 11:01:01 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:01:01 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:01:01 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:01:01 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:01:01 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:01:01 <1b>[0mTraining LinearSVM Classification...<1b>[0m [train]
2026-10-05 11:01:01 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:01:01 <1b>[0mOne hot encoding gn... ✔ [one_hot]
2026-10-05 11:01:01 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLinearSVM<1b>[0m (Support Vector Machine with Linear Kernel)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 45 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 2 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.957
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.999
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.023
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.041
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:01:01 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.35 seconds.<1b>[0m [train]
2026-10-05 11:01:01 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:01:01 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:01:01 <1b>[0mTraining set: 135 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 11:01:01 <1b>[0m Test set: 15 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 11:01:01 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:01:01 <1b>[0mTraining LinearSVM Classification...<1b>[0m [train]
2026-10-05 11:01:01 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLinearSVM<1b>[0m (Support Vector Machine with Linear Kernel)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 45 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 42 3
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 1 44
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.970
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.970
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.970
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 0.933 0.978
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 0.989 0.967
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000 0.961 0.972
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 0.977 0.936
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 0.967 0.989
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000 0.955 0.957
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 5 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 5 0
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 0 5
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 1.000
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000 1.000 1.000
2026-10-05 11:01:02 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.33 seconds.<1b>[0m [train]
2026-10-05 11:01:02 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:01:02 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:01:02 <1b>[0mTraining set: 100 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:01:02 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:01:02 <1b>[0m<> Training LinearSVM Classification using 3 independent folds...<1b>[0m [train]
2026-10-05 11:01:02 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:01:02 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 11:01:02 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Classification Model>
LinearSVM (Support Vector Machine with Linear Kernel)
⟳ Tested using 3 independent folds.
<Resampled Classification Training Metrics>
Aggregate Confusion Matrix across resamples.
Predicted
Reference virginica versicolor
virginica 100 0
versicolor 6 94
Showing mean (sd) across resamples.
Sensitivity: 1.000 (0.000)
Specificity: 0.940 (0.030)
Balanced Accuracy: 0.970 (0.015)
Ppv: 0.944 (0.027)
Npv: 1.000 (0.000)
F1: 0.971 (0.014)
Accuracy: 0.970 (0.015)
Auc: 0.998 (4.9e-04)
Brier Score: 0.029 (0.006)
<Resampled Classification Test Metrics>
Aggregate Confusion Matrix across resamples.
Predicted
Reference virginica versicolor
virginica 49 1
versicolor 3 47
Showing mean (sd) across resamples.
Sensitivity: 0.979 (0.036)
Specificity: 0.941 (0.059)
Balanced Accuracy: 0.960 (0.016)
Ppv: 0.946 (0.053)
Npv: 0.980 (0.034)
F1: 0.961 (0.015)
Accuracy: 0.960 (0.016)
Auc: 0.998 (4e-03)
Brier Score: 0.034 (1.7e-03)
2026-10-05 11:01:02 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.89 seconds.<1b>[0m [train]
2026-10-05 11:01:02 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:01:03 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:01:03 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:01:03 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:01:03 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:01:03 <1b>[0mTraining RadialSVM Regression...<1b>[0m [train]
2026-10-05 11:01:03 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:01:03 <1b>[0mOne hot encoding g... ✔ [one_hot]
2026-10-05 11:01:03 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mRadialSVM<1b>[0m (Support Vector Machine with Radial Kernel)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.73
<1b>[1m MSE<1b>[22m: 0.84
<1b>[1m RMSE<1b>[22m: 0.92
<1b>[1m R²<1b>[22m: 0.83
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.75
<1b>[1m MSE<1b>[22m: 1.01
<1b>[1m RMSE<1b>[22m: 1.01
<1b>[1m R²<1b>[22m: 0.77
2026-10-05 11:01:03 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.54 seconds.<1b>[0m [train]
2026-10-05 11:01:03 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:01:03 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:01:03 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:01:03 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:01:03 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: c(`_R_CHECK_LIMIT_CORES_` = 1); Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:01:03 <1b>[0m<> Tuning RadialSVM by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 11:01:03 <1b>[0m3 parameter combinations x 5 resamples: 15 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 11:01:03 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:01:03 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
\ 11/15 ETA: 1s | Tuning... (15 combinations)
2026-10-05 11:01:09 <1b>[0mBest config to minimize mse:<1b>[0m [tune_GridSearch]
cost: {1, 10, 100} => 1
2026-10-05 11:01:09 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 11:01:09 <1b>[0mTraining RadialSVM Regression with tuned hyperparameters...<1b>[0m [train]
2026-10-05 11:01:09 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:01:09 <1b>[0mOne hot encoding g... ✔ [one_hot]
2026-10-05 11:01:09 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mRadialSVM<1b>[0m (Support Vector Machine with Radial Kernel)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.73
<1b>[1m MSE<1b>[22m: 0.84
<1b>[1m RMSE<1b>[22m: 0.92
<1b>[1m R²<1b>[22m: 0.83
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.75
<1b>[1m MSE<1b>[22m: 1.01
<1b>[1m RMSE<1b>[22m: 1.01
<1b>[1m R²<1b>[22m: 0.77
2026-10-05 11:01:10 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 6.41 seconds.<1b>[0m [train]
2026-10-05 11:01:10 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:01:10 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:01:10 <1b>[0mTraining set: 400 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:01:10 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:01:10 <1b>[0m<> Training RadialSVM Regression using 3 independent folds...<1b>[0m [train]
2026-10-05 11:01:10 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:01:11 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Regression Model>
RadialSVM (Support Vector Machine with Radial Kernel)
⟳ Tested using 3 independent folds.
<Resampled Regression Training Metrics>
Showing mean (sd) across resamples.
MAE: 0.729 (0.014)
MSE: 0.861 (0.011)
RMSE: 0.928 (0.006)
R²: 0.824 (2.9e-03)
<Resampled Regression Test Metrics>
Showing mean (sd) across resamples.
MAE: 0.768 (0.032)
MSE: 0.938 (0.030)
RMSE: 0.968 (0.016)
R²: 0.809 (0.006)
2026-10-05 11:01:11 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 1.27 seconds.<1b>[0m [train]
2026-10-05 11:01:11 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:01:11 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:01:11 <1b>[0mTraining set: 400 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:01:11 <1b>[0mTuning parallelization enabled.<1b>[0m [get_n_workers]
2026-10-05 11:01:11 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:01:11 <1b>[0m<> Training RadialSVM Regression using 3 independent folds...<1b>[0m [train]
2026-10-05 11:01:11 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
\ 7/10 ETA: 1s | Tuning... (10 combinations)
2026-10-05 11:01:22 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Regression Model>
RadialSVM (Support Vector Machine with Radial Kernel)
⚙ Tuned using exhaustive grid search.
⟳ Tested using 3 independent folds.
<Resampled Regression Training Metrics>
Showing mean (sd) across resamples.
MAE: 0.713 (0.024)
MSE: 0.837 (0.051)
RMSE: 0.915 (0.028)
R²: 0.829 (0.011)
<Resampled Regression Test Metrics>
Showing mean (sd) across resamples.
MAE: 0.787 (0.005)
MSE: 0.963 (3.8e-03)
RMSE: 0.981 (1.9e-03)
R²: 0.803 (0.007)
2026-10-05 11:01:23 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 12.14 seconds.<1b>[0m [train]
2026-10-05 11:01:23 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:01:23 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:01:23 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:01:23 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:01:23 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:01:23 <1b>[0mTraining RadialSVM Classification...<1b>[0m [train]
2026-10-05 11:01:23 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:01:23 <1b>[0mOne hot encoding gn... ✔ [one_hot]
2026-10-05 11:01:23 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mRadialSVM<1b>[0m (Support Vector Machine with Radial Kernel)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 42 3
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 2 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.933
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.944
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.955
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.935
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.944
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.944
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.995
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.035
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.059
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:01:23 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.32 seconds.<1b>[0m [train]
2026-10-05 11:01:24 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:01:24 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:01:24 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:01:24 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:01:24 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:01:24 <1b>[0m<> Tuning RadialSVM by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 11:01:24 <1b>[0m2 parameter combinations x 5 resamples: 10 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 11:01:24 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:01:24 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 11:01:24 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
2026-10-05 11:01:26 <1b>[0mBest config to maximize balanced_accuracy:<1b>[0m [tune_GridSearch]
cost: {1, 10} => 10
2026-10-05 11:01:26 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 11:01:26 <1b>[0mTraining RadialSVM Classification with tuned hyperparameters...<1b>[0m [train]
2026-10-05 11:01:26 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:01:26 <1b>[0mOne hot encoding gn... ✔ [one_hot]
2026-10-05 11:01:26 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mRadialSVM<1b>[0m (Support Vector Machine with Radial Kernel)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 44 1
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 2 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.967
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.957
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.977
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.967
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.967
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.999
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.022
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.049
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:01:26 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 2.71 seconds.<1b>[0m [train]
2026-10-05 11:01:26 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:01:26 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:01:26 <1b>[0mTraining set: 100 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:01:26 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:01:26 <1b>[0m<> Training RadialSVM Classification using 3 independent folds...<1b>[0m [train]
2026-10-05 11:01:26 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:01:26 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 11:01:27 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Classification Model>
RadialSVM (Support Vector Machine with Radial Kernel)
⟳ Tested using 3 independent folds.
<Resampled Classification Training Metrics>
Aggregate Confusion Matrix across resamples.
Predicted
Reference virginica versicolor
virginica 93 7
versicolor 4 96
Showing mean (sd) across resamples.
Sensitivity: 0.930 (0.018)
Specificity: 0.960 (0.045)
Balanced Accuracy: 0.945 (0.016)
Ppv: 0.961 (0.043)
Npv: 0.932 (0.014)
F1: 0.945 (0.015)
Accuracy: 0.945 (0.016)
Auc: 0.992 (4.2e-03)
Brier Score: 0.044 (0.008)
<Resampled Classification Test Metrics>
Aggregate Confusion Matrix across resamples.
Predicted
Reference virginica versicolor
virginica 45 5
versicolor 4 46
Showing mean (sd) across resamples.
Sensitivity: 0.901 (0.067)
Specificity: 0.920 (0.033)
Balanced Accuracy: 0.911 (0.028)
Ppv: 0.920 (0.027)
Npv: 0.906 (0.055)
F1: 0.909 (0.032)
Accuracy: 0.911 (0.028)
Auc: 0.979 (0.021)
Brier Score: 0.056 (0.020)
2026-10-05 11:01:27 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.76 seconds.<1b>[0m [train]
2026-10-05 11:01:27 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:01:27 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:01:27 <1b>[0mTraining set: 100 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:01:27 <1b>[0mTuning parallelization enabled.<1b>[0m [get_n_workers]
2026-10-05 11:01:27 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:01:27 <1b>[0m<> Training RadialSVM Classification using 3 independent folds...<1b>[0m [train]
2026-10-05 11:01:27 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:01:27 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
\ 2/3 ETA: 2s | Training outer resamples...
2026-10-05 11:01:34 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Classification Model>
RadialSVM (Support Vector Machine with Radial Kernel)
⚙ Tuned using exhaustive grid search.
⟳ Tested using 3 independent folds.
<Resampled Classification Training Metrics>
Aggregate Confusion Matrix across resamples.
Predicted
Reference virginica versicolor
virginica 97 3
versicolor 2 98
Showing mean (sd) across resamples.
Sensitivity: 0.970 (5.1e-04)
Specificity: 0.980 (0.017)
Balanced Accuracy: 0.975 (0.009)
Ppv: 0.980 (0.017)
Npv: 0.970 (1e-03)
F1: 0.975 (0.009)
Accuracy: 0.975 (0.009)
Auc: 0.998 (1.4e-03)
Brier Score: 0.024 (0.006)
<Resampled Classification Test Metrics>
Aggregate Confusion Matrix across resamples.
Predicted
Reference virginica versicolor
virginica 47 3
versicolor 3 47
Showing mean (sd) across resamples.
Sensitivity: 0.941 (0.102)
Specificity: 0.939 (0.063)
Balanced Accuracy: 0.940 (0.029)
Ppv: 0.944 (0.056)
Npv: 0.950 (0.087)
F1: 0.939 (0.034)
Accuracy: 0.940 (0.029)
Auc: 0.989 (0.007)
Brier Score: 0.048 (0.007)
2026-10-05 11:01:34 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 7.12 seconds.<1b>[0m [train]
2026-10-05 11:01:34 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:01:34 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:01:34 <1b>[0mTraining set: 135 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 11:01:34 <1b>[0m Test set: 15 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 11:01:34 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:01:34 <1b>[0mTraining RadialSVM Classification...<1b>[0m [train]
2026-10-05 11:01:34 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mRadialSVM<1b>[0m (Support Vector Machine with Radial Kernel)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 45 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 39 6
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 6 39
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.911
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.911
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.911
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 0.867 0.867
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 0.933 0.933
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000 0.900 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 0.867 0.867
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 0.933 0.933
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000 0.867 0.867
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 5 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 5 0
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 2 3
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.867
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.861
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.867
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 1.000 0.600
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 0.800 1.000
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000 0.900 0.800
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 0.714 1.000
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 1.000 0.833
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000 0.833 0.750
2026-10-05 11:01:35 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.29 seconds.<1b>[0m [train]
2026-10-05 11:01:35 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:01:35 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:01:35 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:01:35 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:01:35 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:01:35 <1b>[0mTraining CART Regression...<1b>[0m [train]
2026-10-05 11:01:35 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mCART<1b>[0m (Classification and Regression Trees)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.84
<1b>[1m MSE<1b>[22m: 1.13
<1b>[1m RMSE<1b>[22m: 1.06
<1b>[1m R²<1b>[22m: 0.77
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 1.08
<1b>[1m MSE<1b>[22m: 1.80
<1b>[1m RMSE<1b>[22m: 1.34
<1b>[1m R²<1b>[22m: 0.60
2026-10-05 11:01:35 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.19 seconds.<1b>[0m [train]
2026-10-05 11:01:35 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:01:35 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:01:35 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:01:35 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:01:35 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:01:35 <1b>[0m<> Tuning CART by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 11:01:35 <1b>[0m2 parameter combinations x 5 resamples: 10 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 11:01:35 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:01:35 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
\ 9/10 ETA: 0s | Tuning... (10 combinations)
2026-10-05 11:01:38 <1b>[0mBest config to minimize mse:<1b>[0m [tune_GridSearch]
maxdepth: {2, 3} => 3
2026-10-05 11:01:38 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 11:01:38 <1b>[0mTraining CART Regression with tuned hyperparameters...<1b>[0m [train]
2026-10-05 11:01:38 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mCART<1b>[0m (Classification and Regression Trees)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.93
<1b>[1m MSE<1b>[22m: 1.43
<1b>[1m RMSE<1b>[22m: 1.19
<1b>[1m R²<1b>[22m: 0.71
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 1.05
<1b>[1m MSE<1b>[22m: 1.59
<1b>[1m RMSE<1b>[22m: 1.26
<1b>[1m R²<1b>[22m: 0.64
2026-10-05 11:01:38 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 2.78 seconds.<1b>[0m [train]
2026-10-05 11:01:38 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:01:38 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:01:38 <1b>[0mTraining set: 400 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:01:38 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:01:38 <1b>[0m<> Training CART Regression using 3 independent folds...<1b>[0m [train]
2026-10-05 11:01:38 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:01:38 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Regression Model>
CART (Classification and Regression Trees)
⟳ Tested using 3 independent folds.
<Resampled Regression Training Metrics>
Showing mean (sd) across resamples.
MAE: 0.788 (0.043)
MSE: 0.981 (0.063)
RMSE: 0.990 (0.032)
R²: 0.800 (0.011)
<Resampled Regression Test Metrics>
Showing mean (sd) across resamples.
MAE: 1.045 (0.051)
MSE: 1.710 (0.156)
RMSE: 1.307 (0.059)
R²: 0.651 (0.033)
2026-10-05 11:01:38 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.67 seconds.<1b>[0m [train]
2026-10-05 11:01:39 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:01:39 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:01:39 <1b>[0mTraining set: 400 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:01:39 <1b>[0mTuning parallelization enabled.<1b>[0m [get_n_workers]
2026-10-05 11:01:39 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:01:39 <1b>[0m<> Training CART Regression using 3 independent folds...<1b>[0m [train]
2026-10-05 11:01:39 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
\ 16/20 ETA: 1s | Tuning... (20 combinations)
\ 10/20 ETA: 2s | Tuning... (20 combinations)
2026-10-05 11:01:52 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Regression Model>
CART (Classification and Regression Trees)
⚙ Tuned using exhaustive grid search.
⟳ Tested using 3 independent folds.
<Resampled Regression Training Metrics>
Showing mean (sd) across resamples.
MAE: 1.134 (0.054)
MSE: 1.944 (0.122)
RMSE: 1.394 (0.044)
R²: 0.604 (0.021)
<Resampled Regression Test Metrics>
Showing mean (sd) across resamples.
MAE: 1.223 (0.050)
MSE: 2.403 (0.145)
RMSE: 1.550 (0.047)
R²: 0.510 (0.025)
2026-10-05 11:01:52 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 13.43 seconds.<1b>[0m [train]
2026-10-05 11:01:52 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:01:52 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:01:52 <1b>[0mTraining set: 400 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:01:52 <1b>[0mTuning parallelization enabled.<1b>[0m [get_n_workers]
2026-10-05 11:01:52 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:01:52 <1b>[0m<> Training CART Regression using 3 independent folds...<1b>[0m [train]
2026-10-05 11:01:52 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:01:58 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Regression Model>
CART (Classification and Regression Trees)
⚙ Tuned using exhaustive grid search.
⟳ Tested using 3 independent folds.
<Resampled Regression Training Metrics>
Showing mean (sd) across resamples.
MAE: 0.804 (0.012)
MSE: 1.022 (0.027)
RMSE: 1.011 (0.013)
R²: 0.791 (0.011)
<Resampled Regression Test Metrics>
Showing mean (sd) across resamples.
MAE: 1.070 (0.022)
MSE: 1.810 (0.049)
RMSE: 1.345 (0.018)
R²: 0.629 (0.028)
2026-10-05 11:01:58 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 6.41 seconds.<1b>[0m [train]
2026-10-05 11:01:58 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:01:59 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:01:59 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:01:59 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:01:59 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: c(`_R_CHECK_LIMIT_CORES_` = 1); Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:01:59 <1b>[0m<> Tuning CART by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 11:01:59 <1b>[0m2 parameter combinations x 5 resamples: 10 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 11:01:59 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:01:59 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 11:01:59 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
2026-10-05 11:02:01 <1b>[0mBest config to maximize balanced_accuracy:<1b>[0m [tune_GridSearch]
maxdepth: {1, 2} => 2
2026-10-05 11:02:01 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 11:02:01 <1b>[0mTraining CART Classification with tuned hyperparameters...<1b>[0m [train]
2026-10-05 11:02:01 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mCART<1b>[0m (Classification and Regression Trees)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 45 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 2 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.957
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.987
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.020
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.096
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:02:01 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 2.46 seconds.<1b>[0m [train]
2026-10-05 11:02:01 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:02:01 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:02:01 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:02:01 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:02:01 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:02:01 <1b>[0mCalculating case weights using Inverse Frequency Weighting.<1b>[0m [ifw]
2026-10-05 11:02:01 <1b>[0mTraining CART Classification...<1b>[0m [train]
2026-10-05 11:02:01 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mCART<1b>[0m (Classification and Regression Trees)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 45 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 45
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Auc<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.000
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.100
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:02:01 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.31 seconds.<1b>[0m [train]
2026-10-05 11:02:01 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:02:01 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:02:01 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:02:01 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:02:01 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:02:01 <1b>[0m<> Tuning CART by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 11:02:01 <1b>[0m2 parameter combinations x 5 resamples: 10 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 11:02:01 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:02:01 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 11:02:01 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
2026-10-05 11:02:04 <1b>[0mBest config to maximize balanced_accuracy:<1b>[0m [tune_GridSearch]
maxdepth: {1, 2} => 2
2026-10-05 11:02:04 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 11:02:04 <1b>[0mTraining CART Classification with tuned hyperparameters...<1b>[0m [train]
2026-10-05 11:02:04 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mCART<1b>[0m (Classification and Regression Trees)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 45 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 2 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.957
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.987
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.020
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.096
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:02:04 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 2.60 seconds.<1b>[0m [train]
2026-10-05 11:02:04 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:02:04 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:02:04 <1b>[0mTraining set: 100 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:02:04 <1b>[0mTuning parallelization enabled.<1b>[0m [get_n_workers]
2026-10-05 11:02:04 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:02:04 <1b>[0m<> Training CART Classification using 3 independent folds...<1b>[0m [train]
2026-10-05 11:02:04 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:02:04 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 11:02:11 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Classification Model>
CART (Classification and Regression Trees)
⚙ Tuned using exhaustive grid search.
⟳ Tested using 3 independent folds.
<Resampled Classification Training Metrics>
Aggregate Confusion Matrix across resamples.
Predicted
Reference virginica versicolor
virginica 94 6
versicolor 5 95
Showing mean (sd) across resamples.
Sensitivity: 0.941 (0.059)
Specificity: 0.949 (0.063)
Balanced Accuracy: 0.945 (0.008)
Ppv: 0.954 (0.056)
Npv: 0.945 (0.053)
F1: 0.945 (0.008)
Accuracy: 0.945 (0.008)
Auc: 0.946 (0.008)
Brier Score: 0.050 (0.006)
<Resampled Classification Test Metrics>
Aggregate Confusion Matrix across resamples.
Predicted
Reference virginica versicolor
virginica 46 4
versicolor 4 46
Showing mean (sd) across resamples.
Sensitivity: 0.920 (0.089)
Specificity: 0.920 (0.089)
Balanced Accuracy: 0.920 (0.015)
Ppv: 0.929 (0.075)
Npv: 0.929 (0.075)
F1: 0.920 (0.017)
Accuracy: 0.920 (0.015)
Auc: 0.911 (0.028)
Brier Score: 0.075 (0.011)
2026-10-05 11:02:11 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 7.25 seconds.<1b>[0m [train]
2026-10-05 11:02:11 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:02:11 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:02:11 <1b>[0mTraining set: 135 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 11:02:11 <1b>[0m Test set: 15 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 11:02:11 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:02:11 <1b>[0mTraining CART Classification...<1b>[0m [train]
2026-10-05 11:02:11 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mCART<1b>[0m (Classification and Regression Trees)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 45 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 44 1
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 0 45
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.993
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.993
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.993
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 0.978 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 1.000 0.989
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000 0.989 0.994
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 1.000 0.978
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 0.989 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000 0.989 0.989
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 5 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 5 0
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 0 5
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 1.000
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000 1.000 1.000
2026-10-05 11:02:12 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.37 seconds.<1b>[0m [train]
2026-10-05 11:02:12 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:02:12 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:02:12 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:02:12 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:02:12 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:02:12 <1b>[0mTraining LightCART Regression...<1b>[0m [train]
2026-10-05 11:02:12 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:02:12 <1b>[0mConverting 1 factor to integer...<1b>[0m [preprocess]
2026-10-05 11:02:12 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightCART<1b>[0m (Decision Tree)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 1.66
<1b>[1m MSE<1b>[22m: 4.24
<1b>[1m RMSE<1b>[22m: 2.06
<1b>[1m R²<1b>[22m: 0.15
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 1.58
<1b>[1m MSE<1b>[22m: 3.75
<1b>[1m RMSE<1b>[22m: 1.94
<1b>[1m R²<1b>[22m: 0.16
2026-10-05 11:02:12 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.63 seconds.<1b>[0m [train]
2026-10-05 11:02:13 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:02:13 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:02:13 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:02:13 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:02:13 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:02:13 <1b>[0mTraining LightCART Regression...<1b>[0m [train]
2026-10-05 11:02:13 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:02:13 <1b>[0mConverting 1 factor to integer...<1b>[0m [preprocess]
2026-10-05 11:02:13 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightCART<1b>[0m (Decision Tree)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 1.66
<1b>[1m MSE<1b>[22m: 4.24
<1b>[1m RMSE<1b>[22m: 2.06
<1b>[1m R²<1b>[22m: 0.15
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 1.58
<1b>[1m MSE<1b>[22m: 3.75
<1b>[1m RMSE<1b>[22m: 1.94
<1b>[1m R²<1b>[22m: 0.16
2026-10-05 11:02:13 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.51 seconds.<1b>[0m [train]
2026-10-05 11:02:13 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:02:13 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:02:13 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:02:13 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:02:13 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:02:13 <1b>[0mTraining LightCART Classification...<1b>[0m [train]
2026-10-05 11:02:13 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:02:13 <1b>[0mConverting 2 factors to integer...<1b>[0m [preprocess]
2026-10-05 11:02:13 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightCART<1b>[0m (Decision Tree)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 41 4
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 44
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.911
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.944
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.976
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.917
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.943
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.944
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.211
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.860
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.219
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:02:14 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.40 seconds.<1b>[0m [train]
2026-10-05 11:02:14 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:02:14 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:02:14 <1b>[0mTraining set: 135 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 11:02:14 <1b>[0m Test set: 15 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 11:02:14 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:02:14 <1b>[0mTraining LightCART Classification...<1b>[0m [train]
2026-10-05 11:02:14 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:02:14 <1b>[0mConverting 1 factor to integer...<1b>[0m [preprocess]
2026-10-05 11:02:14 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightCART<1b>[0m (Decision Tree)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 44 1 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 43 2
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 3 42
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.956
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.956
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.978 0.956 0.933
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 0.956 0.978
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.989 0.956 0.956
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 0.915 0.955
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.989 0.977 0.967
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.989 0.935 0.944
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 5 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 5 0
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.933
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.933
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.933
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 1.000 0.800
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 0.900 1.000
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000 0.950 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 0.833 1.000
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 1.000 0.909
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000 0.909 0.889
2026-10-05 11:02:14 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.55 seconds.<1b>[0m [train]
2026-10-05 11:02:14 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:02:14 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:02:14 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:02:14 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:02:14 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:02:14 <1b>[0mTraining LightRF Regression...<1b>[0m [train]
2026-10-05 11:02:14 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:02:14 <1b>[0mConverting 1 factor to integer...<1b>[0m [preprocess]
2026-10-05 11:02:14 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightRF<1b>[0m (LightGBM Random Forest)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.92
<1b>[1m MSE<1b>[22m: 1.34
<1b>[1m RMSE<1b>[22m: 1.16
<1b>[1m R²<1b>[22m: 0.73
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.89
<1b>[1m MSE<1b>[22m: 1.18
<1b>[1m RMSE<1b>[22m: 1.09
<1b>[1m R²<1b>[22m: 0.73
2026-10-05 11:02:16 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 1.52 seconds.<1b>[0m [train]
2026-10-05 11:02:16 <1b>[0mConverting 1 factor to integer...<1b>[0m [preprocess]
2026-10-05 11:02:16 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
2026-10-05 11:02:16 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:02:16 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:02:16 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:02:16 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:02:16 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:02:16 <1b>[0m<> Tuning LightRF by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 11:02:16 <1b>[0m2 parameter combinations x 5 resamples: 10 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 11:02:16 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:02:16 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
\ 8/10 ETA: 1s | Tuning... (10 combinations)
2026-10-05 11:02:21 <1b>[0mBest config to minimize mse:<1b>[0m [tune_GridSearch]
lambda_l1: {0, 0.1} => 0
2026-10-05 11:02:21 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 11:02:21 <1b>[0mTraining LightRF Regression with tuned hyperparameters...<1b>[0m [train]
2026-10-05 11:02:21 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:02:21 <1b>[0mConverting 1 factor to integer...<1b>[0m [preprocess]
2026-10-05 11:02:21 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightRF<1b>[0m (LightGBM Random Forest)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.92
<1b>[1m MSE<1b>[22m: 1.33
<1b>[1m RMSE<1b>[22m: 1.15
<1b>[1m R²<1b>[22m: 0.73
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.88
<1b>[1m MSE<1b>[22m: 1.17
<1b>[1m RMSE<1b>[22m: 1.08
<1b>[1m R²<1b>[22m: 0.74
2026-10-05 11:02:22 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 6.17 seconds.<1b>[0m [train]
2026-10-05 11:02:22 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:02:22 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:02:22 <1b>[0mTraining set: 400 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:02:22 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:02:22 <1b>[0m<> Training LightRF Regression using 3 independent folds...<1b>[0m [train]
2026-10-05 11:02:22 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
\ 8/10 ETA: 1s | Tuning... (10 combinations)
\ 2/3 ETA: 5s | Training outer resamples...
\ 8/10 ETA: 1s | Tuning... (10 combinations)
\ 2/3 ETA: 5s | Training outer resamples...
2026-10-05 11:02:36 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Regression Model>
LightRF (LightGBM Random Forest)
⚙ Tuned using exhaustive grid search.
⟳ Tested using 3 independent folds.
<Resampled Regression Training Metrics>
Showing mean (sd) across resamples.
MAE: 0.962 (0.025)
MSE: 1.446 (0.126)
RMSE: 1.202 (0.053)
R²: 0.705 (0.030)
<Resampled Regression Test Metrics>
Showing mean (sd) across resamples.
MAE: 1.035 (0.067)
MSE: 1.657 (0.266)
RMSE: 1.284 (0.103)
R²: 0.661 (0.064)
2026-10-05 11:02:37 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 14.30 seconds.<1b>[0m [train]
2026-10-05 11:02:37 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:02:37 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:02:37 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:02:37 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:02:37 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:02:37 <1b>[0mTraining LightRF Classification...<1b>[0m [train]
2026-10-05 11:02:37 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:02:37 <1b>[0mConverting 2 factors to integer...<1b>[0m [preprocess]
2026-10-05 11:02:37 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightRF<1b>[0m (LightGBM Random Forest)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 41 4
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 44
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.911
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.944
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.976
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.917
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.943
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.944
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.996
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.048
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.092
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:02:37 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.40 seconds.<1b>[0m [train]
2026-10-05 11:02:37 <1b>[0mConverting 1 factor to integer...<1b>[0m [preprocess]
2026-10-05 11:02:37 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
2026-10-05 11:02:37 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:02:37 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:02:37 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:02:37 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:02:37 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:02:37 <1b>[0m<> Tuning LightRF by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 11:02:37 <1b>[0m2 parameter combinations x 5 resamples: 10 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 11:02:37 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:02:37 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 11:02:37 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
2026-10-05 11:02:40 <1b>[0mBest config to maximize balanced_accuracy:<1b>[0m [tune_GridSearch]
max_depth: {-1, 5} => -1
2026-10-05 11:02:40 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 11:02:40 <1b>[0mTraining LightRF Classification with tuned hyperparameters...<1b>[0m [train]
2026-10-05 11:02:40 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:02:40 <1b>[0mConverting 2 factors to integer...<1b>[0m [preprocess]
2026-10-05 11:02:40 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightRF<1b>[0m (LightGBM Random Forest)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 41 4
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 44
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.911
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.944
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.976
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.917
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.943
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.944
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.996
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.048
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.092
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:02:40 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 3.05 seconds.<1b>[0m [train]
2026-10-05 11:02:40 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:02:40 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:02:40 <1b>[0mTraining set: 100 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:02:40 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:02:40 <1b>[0m<> Training LightRF Classification using 3 independent folds...<1b>[0m [train]
2026-10-05 11:02:40 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:02:40 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 11:02:42 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Classification Model>
LightRF (LightGBM Random Forest)
⟳ Tested using 3 independent folds.
<Resampled Classification Training Metrics>
Aggregate Confusion Matrix across resamples.
Predicted
Reference virginica versicolor
virginica 95 5
versicolor 4 96
Showing mean (sd) across resamples.
Sensitivity: 0.950 (0.018)
Specificity: 0.960 (0.045)
Balanced Accuracy: 0.955 (0.014)
Ppv: 0.962 (0.042)
Npv: 0.951 (0.015)
F1: 0.955 (0.013)
Accuracy: 0.955 (0.014)
Auc: 0.988 (0.013)
Brier Score: 0.111 (0.022)
<Resampled Classification Test Metrics>
Aggregate Confusion Matrix across resamples.
Predicted
Reference virginica versicolor
virginica 45 5
versicolor 3 47
Showing mean (sd) across resamples.
Sensitivity: 0.901 (0.067)
Specificity: 0.941 (0.102)
Balanced Accuracy: 0.921 (0.044)
Ppv: 0.947 (0.091)
Npv: 0.908 (0.051)
F1: 0.920 (0.042)
Accuracy: 0.921 (0.044)
Auc: 0.976 (0.030)
Brier Score: 0.115 (0.038)
2026-10-05 11:02:42 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 1.62 seconds.<1b>[0m [train]
2026-10-05 11:02:42 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:02:42 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:02:42 <1b>[0mTraining set: 135 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 11:02:42 <1b>[0m Test set: 15 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 11:02:42 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:02:42 <1b>[0mTraining LightRF Classification...<1b>[0m [train]
2026-10-05 11:02:42 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:02:42 <1b>[0mConverting 1 factor to integer...<1b>[0m [preprocess]
2026-10-05 11:02:42 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightRF<1b>[0m (LightGBM Random Forest)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 45 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 42 2
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 3 42
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.956
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.955
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.956
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 0.933 0.933
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.989 0.967 0.978
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.994 0.950 0.956
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.978 0.933 0.955
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 0.967 0.967
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.989 0.933 0.944
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 5 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 5 0
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.933
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.933
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.933
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 1.000 0.800
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 0.900 1.000
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000 0.950 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 0.833 1.000
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 1.000 0.909
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000 0.909 0.889
2026-10-05 11:02:43 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 1.22 seconds.<1b>[0m [train]
2026-10-05 11:02:43 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:02:43 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:02:43 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:02:43 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:02:43 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:02:43 <1b>[0mTraining LightGBM Regression...<1b>[0m [train]
2026-10-05 11:02:43 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:02:43 <1b>[0mConverting 1 factor to integer...<1b>[0m [preprocess]
2026-10-05 11:02:43 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightGBM<1b>[0m (Gradient Boosting)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 1.29
<1b>[1m MSE<1b>[22m: 2.63
<1b>[1m RMSE<1b>[22m: 1.62
<1b>[1m R²<1b>[22m: 0.47
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 1.20
<1b>[1m MSE<1b>[22m: 2.33
<1b>[1m RMSE<1b>[22m: 1.53
<1b>[1m R²<1b>[22m: 0.48
2026-10-05 11:02:44 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 1.01 seconds.<1b>[0m [train]
2026-10-05 11:02:44 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:02:44 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:02:44 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:02:44 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:02:44 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:02:44 <1b>[0m<> Tuning LightGBM by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 11:02:44 <1b>[0m1 parameter combination x 5 resamples: 5 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 11:02:44 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:02:44 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
\ 2/5 ETA: 4s | Tuning... (5 combinations)
| 4/5 ETA: 2s | Tuning... (5 combinations)
2026-10-05 11:02:51 <1b>[0mBest config to minimize mse:<1b>[0m [tune_GridSearch]
nrounds: {} => 434
2026-10-05 11:02:51 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 11:02:51 <1b>[0mTraining LightGBM Regression with tuned hyperparameters...<1b>[0m [train]
2026-10-05 11:02:51 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:02:51 <1b>[0mConverting 1 factor to integer...<1b>[0m [preprocess]
2026-10-05 11:02:51 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightGBM<1b>[0m (Gradient Boosting)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.60
<1b>[1m MSE<1b>[22m: 0.58
<1b>[1m RMSE<1b>[22m: 0.76
<1b>[1m R²<1b>[22m: 0.88
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.80
<1b>[1m MSE<1b>[22m: 1.19
<1b>[1m RMSE<1b>[22m: 1.09
<1b>[1m R²<1b>[22m: 0.73
2026-10-05 11:03:01 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 17.17 seconds.<1b>[0m [train]
2026-10-05 11:03:01 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:03:01 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:03:01 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:03:01 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:03:01 <1b>[0m<> Training LightGBM Regression using 3 independent folds...<1b>[0m [train]
2026-10-05 11:03:01 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:03:09 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Regression Model>
LightGBM (Gradient Boosting)
⚙ Tuned using exhaustive grid search.
⟳ Tested using 3 independent folds.
<Resampled Regression Training Metrics>
Showing mean (sd) across resamples.
MAE: 1.287 (0.027)
MSE: 2.609 (0.113)
RMSE: 1.615 (0.035)
R²: 0.473 (0.006)
<Resampled Regression Test Metrics>
Showing mean (sd) across resamples.
MAE: 1.336 (0.047)
MSE: 2.820 (0.253)
RMSE: 1.678 (0.075)
R²: 0.429 (0.020)
2026-10-05 11:03:09 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 7.68 seconds.<1b>[0m [train]
2026-10-05 11:03:09 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:03:09 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:03:09 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:03:09 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:03:09 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:03:09 <1b>[0m<> Tuning LightGBM by exhaustive grid search with 3 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 11:03:09 <1b>[0m1 parameter combination x 3 resamples: 3 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 11:03:09 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:03:09 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 11:03:09 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
2026-10-05 11:03:11 <1b>[0mBest config to maximize balanced_accuracy:<1b>[0m [tune_GridSearch]
nrounds: {} => 375
2026-10-05 11:03:11 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 11:03:11 <1b>[0mTraining LightGBM Classification with tuned hyperparameters...<1b>[0m [train]
2026-10-05 11:03:11 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:03:11 <1b>[0mConverting 2 factors to integer...<1b>[0m [preprocess]
2026-10-05 11:03:11 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightGBM<1b>[0m (Gradient Boosting)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 45 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 2 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.957
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.999
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.017
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.097
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:03:16 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 7.01 seconds.<1b>[0m [train]
2026-10-05 11:03:16 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:03:16 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:03:16 <1b>[0mTraining set: 135 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 11:03:16 <1b>[0m Test set: 15 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 11:03:16 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:03:16 <1b>[0mTraining LightGBM Classification...<1b>[0m [train]
2026-10-05 11:03:16 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:03:16 <1b>[0mConverting 1 factor to integer...<1b>[0m [preprocess]
2026-10-05 11:03:16 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightGBM<1b>[0m (Gradient Boosting)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 45 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 43 2
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 3 42
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.963
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.963
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.963
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 0.956 0.933
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 0.967 0.978
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000 0.961 0.956
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 0.935 0.955
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 0.978 0.967
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000 0.945 0.944
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 5 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 5 0
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.933
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.933
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.933
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 1.000 0.800
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 0.900 1.000
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000 0.950 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 0.833 1.000
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 1.000 0.909
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000 0.909 0.889
2026-10-05 11:03:17 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 1.02 seconds.<1b>[0m [train]
2026-10-05 11:03:17 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:03:17 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:03:17 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:03:17 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:03:17 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:03:17 <1b>[0mTraining LightRuleFit Regression...<1b>[0m [train]
2026-10-05 11:03:17 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:03:17 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:03:17 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:03:17 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:03:17 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:03:17 <1b>[0mTraining LightGBM Regression...<1b>[0m [train]
2026-10-05 11:03:17 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:03:17 <1b>[0mConverting 1 factor to integer...<1b>[0m [preprocess]
2026-10-05 11:03:17 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightGBM<1b>[0m (Gradient Boosting)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.78
<1b>[1m MSE<1b>[22m: 0.96
<1b>[1m RMSE<1b>[22m: 0.98
<1b>[1m R²<1b>[22m: 0.81
2026-10-05 11:03:18 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.75 seconds.<1b>[0m [train]
2026-10-05 11:03:18 <1b>[0mExtracting LightGBM rules... ✔ [extract_rules]
2026-10-05 11:03:18 <1b>[0mExtracted 180 unique rules.<1b>[0m [extract_rules]
2026-10-05 11:03:18 <1b>[0mMatching180rules to358cases... ✔ [match_cases_by_rules]
2026-10-05 11:03:19 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:03:19 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:03:19 <1b>[0mTraining set: 358 cases x 180 features.<1b>[0m [summarize_supervised]
2026-10-05 11:03:19 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:03:19 <1b>[0mTraining GLMNET Regression...<1b>[0m [train]
2026-10-05 11:03:19 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLMNET<1b>[0m (Elastic Net)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.64
<1b>[1m MSE<1b>[22m: 0.65
<1b>[1m RMSE<1b>[22m: 0.80
<1b>[1m R²<1b>[22m: 0.87
2026-10-05 11:03:19 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.30 seconds.<1b>[0m [train]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightRuleFit<1b>[0m (LightGBM RuleFit)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.64
<1b>[1m MSE<1b>[22m: 0.65
<1b>[1m RMSE<1b>[22m: 0.80
<1b>[1m R²<1b>[22m: 0.87
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.82
<1b>[1m MSE<1b>[22m: 1.25
<1b>[1m RMSE<1b>[22m: 1.12
<1b>[1m R²<1b>[22m: 0.72
2026-10-05 11:03:20 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 3.16 seconds.<1b>[0m [train]
2026-10-05 11:03:21 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:03:21 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:03:21 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:03:21 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:03:21 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:03:21 <1b>[0mTraining LightRuleFit Classification...<1b>[0m [train]
2026-10-05 11:03:21 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:03:21 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:03:21 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:03:21 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:03:21 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:03:21 <1b>[0mTraining LightGBM Classification...<1b>[0m [train]
2026-10-05 11:03:21 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:03:21 <1b>[0mConverting 2 factors to integer...<1b>[0m [preprocess]
2026-10-05 11:03:21 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightGBM<1b>[0m (Gradient Boosting)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 45 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 2 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.957
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.998
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.019
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:03:24 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 3.44 seconds.<1b>[0m [train]
2026-10-05 11:03:24 <1b>[0mExtracting LightGBM rules... ✔ [extract_rules]
2026-10-05 11:03:24 <1b>[0mExtracted 12 unique rules.<1b>[0m [extract_rules]
2026-10-05 11:03:24 <1b>[0mMatching12rules to90cases... ✔ [match_cases_by_rules]
2026-10-05 11:03:24 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:03:24 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:03:24 <1b>[0mTraining set: 90 cases x 12 features.<1b>[0m [summarize_supervised]
2026-10-05 11:03:24 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:03:24 <1b>[0m<> Tuning GLMNET by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 11:03:24 <1b>[0m1 parameter combination x 5 resamples: 5 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 11:03:24 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:03:24 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 11:03:24 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
2026-10-05 11:03:28 <1b>[0mBest config to maximize balanced_accuracy:<1b>[0m [tune_GridSearch]
lambda: {} => 0.0658148865916772
2026-10-05 11:03:28 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 11:03:28 <1b>[0mCalculating case weights using Inverse Frequency Weighting.<1b>[0m [ifw]
2026-10-05 11:03:28 <1b>[0mTraining GLMNET Classification with tuned hyperparameters...<1b>[0m [train]
2026-10-05 11:03:28 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLMNET<1b>[0m (Elastic Net)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 45 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 2 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.957
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.996
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.028
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:03:28 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 4.09 seconds.<1b>[0m [train]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightRuleFit<1b>[0m (LightGBM RuleFit)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 45 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 2 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.957
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.996
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.028
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.094
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:03:29 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 8.35 seconds.<1b>[0m [train]
2026-10-05 11:03:33 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:03:33 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:03:33 <1b>[0mTraining set: 50 cases x 1 features.<1b>[0m [summarize_supervised]
2026-10-05 11:03:33 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:03:33 <1b>[0mTraining Isotonic Regression...<1b>[0m [train]
2026-10-05 11:03:33 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mIsotonic<1b>[0m (Isotonic Regression)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.51
<1b>[1m MSE<1b>[22m: 0.61
<1b>[1m RMSE<1b>[22m: 0.78
<1b>[1m R²<1b>[22m: 0.99
2026-10-05 11:03:33 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.11 seconds.<1b>[0m [train]
2026-10-05 11:03:33 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:03:33 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:03:33 <1b>[0mTraining set: 200 cases x 1 features.<1b>[0m [summarize_supervised]
2026-10-05 11:03:33 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:03:33 <1b>[0mTraining Isotonic Classification...<1b>[0m [train]
2026-10-05 11:03:33 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mIsotonic<1b>[0m (Isotonic Regression)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mb <1b>[0m<1b>[1;38;2;108;163;160ma <1b>[0m
<1b>[1;38;2;108;163;160m b<1b>[0m 90 6
<1b>[1;38;2;108;163;160m a<1b>[0m 12 92
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.938
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.885
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.911
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.882
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.939
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.910
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.057
Positive Class <1b>[1;38;2;108;163;160mb<1b>[0m
2026-10-05 11:03:33 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.30 seconds.<1b>[0m [train]
2026-10-05 11:03:33 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:03:33 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:03:33 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:03:33 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:03:33 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:03:33 <1b>[0mTraining Ranger Regression...<1b>[0m [train]
2026-10-05 11:03:33 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mRanger<1b>[0m (Random Forest)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.40
<1b>[1m MSE<1b>[22m: 0.26
<1b>[1m RMSE<1b>[22m: 0.51
<1b>[1m R²<1b>[22m: 0.95
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.89
<1b>[1m MSE<1b>[22m: 1.29
<1b>[1m RMSE<1b>[22m: 1.13
<1b>[1m R²<1b>[22m: 0.71
2026-10-05 11:03:34 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.40 seconds.<1b>[0m [train]
2026-10-05 11:03:34 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:03:34 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:03:34 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:03:34 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:03:34 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:03:34 <1b>[0m<> Tuning Ranger by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 11:03:34 <1b>[0m2 parameter combinations x 5 resamples: 10 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 11:03:34 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:03:34 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
\ 2/10 ETA: 14s | Tuning... (10 combinations)
2026-10-05 11:03:39 <1b>[0mBest config to minimize mse:<1b>[0m [tune_GridSearch]
mtry: {3, 6} => 6
2026-10-05 11:03:39 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 11:03:39 <1b>[0mTraining Ranger Regression with tuned hyperparameters...<1b>[0m [train]
2026-10-05 11:03:39 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mRanger<1b>[0m (Random Forest)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.34
<1b>[1m MSE<1b>[22m: 0.20
<1b>[1m RMSE<1b>[22m: 0.44
<1b>[1m R²<1b>[22m: 0.96
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.91
<1b>[1m MSE<1b>[22m: 1.38
<1b>[1m RMSE<1b>[22m: 1.18
<1b>[1m R²<1b>[22m: 0.69
2026-10-05 11:03:39 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 5.66 seconds.<1b>[0m [train]
2026-10-05 11:03:39 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:03:39 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:03:39 <1b>[0mTraining set: 400 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:03:39 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:03:39 <1b>[0m<> Training Ranger Regression using 3 independent folds...<1b>[0m [train]
2026-10-05 11:03:39 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
\ 1/3 ETA: 10s | Training outer resamples...
2026-10-05 11:03:57 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Regression Model>
Ranger (Random Forest)
⟳ Tested using 3 independent folds.
<Resampled Regression Training Metrics>
Showing mean (sd) across resamples.
MAE: 0.412 (3.1e-03)
MSE: 0.264 (4.9e-03)
RMSE: 0.514 (4.8e-03)
R²: 0.946 (1.7e-03)
<Resampled Regression Test Metrics>
Showing mean (sd) across resamples.
MAE: 0.878 (3.4e-03)
MSE: 1.188 (0.009)
RMSE: 1.090 (4e-03)
R²: 0.758 (0.007)
2026-10-05 11:03:57 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 17.77 seconds.<1b>[0m [train]
| 2/3 ETA: 9s | Training outer resamples...
2026-10-05 11:03:57 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:03:57 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:03:57 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:03:57 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:03:57 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:03:57 <1b>[0mTraining Ranger Classification...<1b>[0m [train]
2026-10-05 11:03:57 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mRanger<1b>[0m (Random Forest)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 44 1
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 2 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.967
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.957
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.977
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.967
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.967
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.998
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.024
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.071
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:03:58 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.28 seconds.<1b>[0m [train]
2026-10-05 11:03:58 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:03:58 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:03:58 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:03:58 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:03:58 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:03:58 <1b>[0m<> Tuning Ranger by exhaustive grid search with 5 independent folds...<1b>[0m [tune_GridSearch]
2026-10-05 11:03:58 <1b>[0m2 parameter combinations x 5 resamples: 10 models total (x86_64-pc-linux-gnu).<1b>[0m [tune_GridSearch]
2026-10-05 11:03:58 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:03:58 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 11:03:58 <1b>[0mTuning in sequence<1b>[0m [tune_GridSearch]
2026-10-05 11:04:00 <1b>[0mBest config to maximize balanced_accuracy:<1b>[0m [tune_GridSearch]
mtry: {2, 4} => 4
2026-10-05 11:04:00 <1b>[0m</> Tuning done.<1b>[0m [tune_GridSearch]
2026-10-05 11:04:00 <1b>[0mTraining Ranger Classification with tuned hyperparameters...<1b>[0m [train]
2026-10-05 11:04:00 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mRanger<1b>[0m (Random Forest)
<1b>[1;38;2;243;132;255m⚙<1b>[0m Tuned using exhaustive grid search.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 43 2
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 2 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.956
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.997
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.025
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.051
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:04:00 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 2.38 seconds.<1b>[0m [train]
2026-10-05 11:04:00 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:04:00 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:04:00 <1b>[0mTraining set: 100 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:04:00 <1b>[0m// Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:04:00 <1b>[0m<> Training Ranger Classification using 3 independent folds...<1b>[0m [train]
2026-10-05 11:04:00 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:04:00 <1b>[0mUsing max n bins possible = 2.<1b>[0m [kfold]
2026-10-05 11:04:01 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Classification Model>
Ranger (Random Forest)
⟳ Tested using 3 independent folds.
<Resampled Classification Training Metrics>
Aggregate Confusion Matrix across resamples.
Predicted
Reference virginica versicolor
virginica 97 3
versicolor 3 97
Showing mean (sd) across resamples.
Sensitivity: 0.970 (0.029)
Specificity: 0.970 (0.029)
Balanced Accuracy: 0.970 (0.025)
Ppv: 0.971 (0.029)
Npv: 0.971 (0.029)
F1: 0.970 (0.025)
Accuracy: 0.970 (0.025)
Auc: 0.997 (2.7e-03)
Brier Score: 0.021 (0.011)
<Resampled Classification Test Metrics>
Aggregate Confusion Matrix across resamples.
Predicted
Reference virginica versicolor
virginica 46 4
versicolor 3 47
Showing mean (sd) across resamples.
Sensitivity: 0.920 (0.089)
Specificity: 0.940 (2.1e-03)
Balanced Accuracy: 0.930 (0.045)
Ppv: 0.938 (0.006)
Npv: 0.927 (0.080)
F1: 0.928 (0.049)
Accuracy: 0.930 (0.045)
Auc: 0.984 (9.8e-04)
Brier Score: 0.065 (0.027)
2026-10-05 11:04:01 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 1.10 seconds.<1b>[0m [train]
2026-10-05 11:04:01 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:04:01 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:04:01 <1b>[0mTraining set: 135 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 11:04:01 <1b>[0m Test set: 15 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 11:04:01 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:04:01 <1b>[0mTraining Ranger Classification...<1b>[0m [train]
2026-10-05 11:04:01 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mRanger<1b>[0m (Random Forest)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 45 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 42 3
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 2 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.963
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.963
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.963
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 0.933 0.956
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 0.978 0.967
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000 0.956 0.961
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 0.955 0.935
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 0.967 0.978
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000 0.944 0.945
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160msetosa <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m<1b>[1;38;2;108;163;160mvirginica <1b>[0m
<1b>[1;38;2;108;163;160m setosa<1b>[0m 5 0 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 0 5 0
<1b>[1;38;2;108;163;160m virginica<1b>[0m 0 0 5
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 1.000
<1b>[1;38;2;108;163;160mSetosa <1b>[0m<1b>[1;38;2;108;163;160mVersicolor <1b>[0m<1b>[1;38;2;108;163;160mVirginica <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000 1.000 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 1.000 1.000 1.000
2026-10-05 11:04:02 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.30 seconds.<1b>[0m [train]
2026-10-05 11:04:02 <1b>[0m<> Calibrating LightRF classification...<1b>[0m [calibrate]
2026-10-05 11:04:02 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:04:02 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:04:02 <1b>[0mTraining set: 90 cases x 1 features.<1b>[0m [summarize_supervised]
2026-10-05 11:04:02 <1b>[0m Test set: 10 cases x 1 features.<1b>[0m [summarize_supervised]
2026-10-05 11:04:02 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:04:02 <1b>[0mTraining Isotonic Classification...<1b>[0m [train]
2026-10-05 11:04:02 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mIsotonic<1b>[0m (Isotonic Regression)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 45 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 2 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.956
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.957
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.997
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.017
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.100
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:04:02 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.30 seconds.<1b>[0m [train]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mLightRF<1b>[0m (LightGBM Random Forest)
<1b>[1;38;2;115;100;242m⟋<1b>[0m Calibrated using Isotonic Regression.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics (Pre => Post Calibration)<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 41 => 45 4 => 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 => 2 44 => 43
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.91 => 1.00
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.98 => 0.96
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.94 => 0.98
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.98 => 0.96
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.92 => 1.00
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.94 => 0.98
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.94 => 0.98
<1b>[22;38;2;128;128;128m Auc<1b>[0m 1.00 => 1.00
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.05 => 0.02
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics (Pre => Post Calibration)<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 => 5 0 => 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 => 1 4 => 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.00 => 1.00
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.80 => 0.80
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.90 => 0.90
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.83 => 0.83
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.00 => 1.00
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.91 => 0.91
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.90 => 0.90
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.90 => 0.90
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.09 => 0.10
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:04:02 <1b>[0m</> Calibration done.<1b>[0m [calibrate]
2026-10-05 11:04:03 <1b>[0mConverting 1 factor to integer...<1b>[0m [preprocess]
2026-10-05 11:04:03 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
2026-10-05 11:04:03 <1b>[0m<> Calibrating LightRF resampled classification...<1b>[0m [calibrate]
<Resampled Classification Model>
LightRF (LightGBM Random Forest)
⟳ Tested using 3 independent folds.
⟋ Calibrated using Isotonic Regression with 5 independent folds.
<Resampled Classification Training Metrics (Pre => Post Calibration)>
Showing mean (sd) across resamples, Pre => Post calibration.
sensitivity: 0.95 (0.02) => 1.00 (0.00)
specificity: 0.96 (0.04) => 0.92 (0.08)
balanced_accuracy: 0.96 (0.01) => 0.96 (0.04)
ppv: 0.96 (0.04) => 0.93 (0.07)
npv: 0.95 (0.02) => 1.00 (0.00)
f1: 0.96 (0.01) => 0.96 (0.04)
accuracy: 0.96 (0.01) => 0.96 (0.04)
auc: 0.99 (0.01) => 0.98 (0.02)
brier_score: 0.11 (0.02) => 0.03 (0.03)
<Resampled Classification Test Metrics (Pre => Post Calibration)>
Showing mean (sd) across resamples, Pre => Post calibration.
sensitivity: 0.90 (0.07) => 0.93 (0.19)
specificity: 0.94 (0.10) => 0.85 (0.20)
balanced_accuracy: 0.92 (0.04) => 0.89 (0.12)
ppv: 0.95 (0.09) => 0.89 (0.14)
npv: 0.91 (0.05) => 0.96 (0.12)
f1: 0.92 (0.04) => 0.89 (0.14)
accuracy: 0.92 (0.04) => 0.89 (0.12)
auc: 0.98 (0.03) => 0.92 (0.11)
brier_score: 0.12 (0.04) => 0.09 (0.11)
2026-10-05 11:04:06 <1b>[0m</> Calibration done.<1b>[0m [calibrate]
Generalized Linear Model was used for regression. R-squared was 0.83 on the training set and 0.77 on the test set.
Generalized Linear Model was used for regression. R-squared was 0.83 on the training set and 0.77 on the test set.
Generalized Linear Model was used for classification. Balanced accuracy was 0.98 in the training set and 0.90 in the test set.
Generalized Linear Model was used for regression. R-squared was 0.83 on the training set and 0.77 on the test set.
Generalized Linear Model was used for classification. Balanced accuracy was 0.98 in the training set and 0.90 in the test set.
Generalized Linear Model was used for regression. Mean R-squared was 0.83 on the training set and 0.82 on the test set across 3 independent folds.
Generalized Linear Model was used for classification. Mean balanced accuracy was 0.99 in the training set and 0.89 in the test set across 3 independent folds.
Generalized Linear Model (GLM) and Classification and Regression Trees (CART) were used for Regression.
The top-performing model was GLM with a test-set Rsq of 0.817, followed by CART with rsq of 0.629 respectively.
Generalized Linear Model (GLM) and Classification and Regression Trees (CART) were used for Classification.
The top-performing model was CART with a test-set Balanced Accuracy of 0.920, followed by GLM with balanced_accuracy of 0.888 respectively.
Generalized Linear Model (GLM) and Classification and Regression Trees (CART) were used for Regression.
The top-performing model was GLM with a test-set Rsq of 0.770, followed by CART with rsq of 0.595 respectively.
2026-10-05 11:04:18 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:04:18 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:04:18 <1b>[0mTraining set: 358 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:04:18 <1b>[0m Test set: 42 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:04:18 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:04:18 <1b>[0mTraining GLM Regression...<1b>[0m [train]
2026-10-05 11:04:18 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLM<1b>[0m (Generalized Linear Model)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.73
<1b>[1m MSE<1b>[22m: 0.82
<1b>[1m RMSE<1b>[22m: 0.91
<1b>[1m R²<1b>[22m: 0.83
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.74
<1b>[1m MSE<1b>[22m: 1.03
<1b>[1m RMSE<1b>[22m: 1.01
<1b>[1m R²<1b>[22m: 0.77
2026-10-05 11:04:19 <1b>[0mWriting data to /tmp/RtmppWvuXM/working_dir/RtmpBt0OZL/file12425fec1b1ec/mod_r_glm...✔ 0.76 secs [rt_save]
2026-10-05 11:04:19 <1b>[0mReload with: > obj <- readRDS('/tmp/RtmppWvuXM/working_dir/RtmpBt0OZL/file12425fec1b1ec/mod_r_glm/train_GLM.rds')<1b>[0m [rt_save]
2026-10-05 11:04:19 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 1.07 seconds.<1b>[0m [train]
2026-10-05 11:04:19 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:04:20 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:04:20 <1b>[0mTraining set: 400 cases x 6 features.<1b>[0m [summarize_supervised]
2026-10-05 11:04:20 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: c(`_R_CHECK_LIMIT_CORES_` = 1) }<1b>[0m [get_n_workers]
2026-10-05 11:04:20 <1b>[0m<> Training GLM Regression using 3 independent folds...<1b>[0m [train]
2026-10-05 11:04:20 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:04:20 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Regression Model>
GLM (Generalized Linear Model)
⟳ Tested using 3 independent folds.
<Resampled Regression Training Metrics>
Showing mean (sd) across resamples.
MAE: 0.723 (0.039)
MSE: 0.831 (0.080)
RMSE: 0.911 (0.044)
R²: 0.830 (0.019)
<Resampled Regression Test Metrics>
Showing mean (sd) across resamples.
MAE: 0.756 (0.096)
MSE: 0.909 (0.188)
RMSE: 0.950 (0.101)
R²: 0.813 (0.046)
2026-10-05 11:04:20 <1b>[0mWriting data to /tmp/RtmppWvuXM/working_dir/RtmpBt0OZL/file12425ffc5689e/resmod_r_glm...✔ 1.2 secs [rt_save]
2026-10-05 11:04:21 <1b>[0mReload with: > obj <- readRDS('/tmp/RtmppWvuXM/working_dir/RtmpBt0OZL/file12425ffc5689e/resmod_r_glm/train_GLM.rds')<1b>[0m [rt_save]
2026-10-05 11:04:21 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 1.83 seconds.<1b>[0m [train]
Classification and Regression Trees (CART), LightGBM Random Forest (LightRF), and Gradient Boosting (LightGBM) were used for Classification.
The top-performing model was CART with a test-set Balanced Accuracy of 0.900, followed by LightRF and LightGBM with balanced_accuracy of 0.900 and 0.900 respectively.
Classification and Regression Trees (CART), LightGBM Random Forest (LightRF), and Gradient Boosting (LightGBM) were used for Classification.
The top-performing model was CART with a test-set Balanced Accuracy of 0.900, followed by LightRF and LightGBM with balanced_accuracy of 0.900 and 0.900 respectively.
Elastic Net (GLMNET), Support Vector Machine with Radial Kernel (RadialSVM), and LightGBM Random Forest (LightRF) were used for Regression.
The top-performing model was RadialSVM with a test-set Rsq of 0.773, followed by GLMNET and LightRF with rsq of 0.772 and 0.735 respectively.
Elastic Net (GLMNET), Support Vector Machine with Radial Kernel (RadialSVM), and LightGBM Random Forest (LightRF) were used for Regression.
The top-performing model was RadialSVM with a test-set Rsq of 0.773, followed by GLMNET and LightRF with rsq of 0.772 and 0.735 respectively.
Generalized Linear Model (GLM), Support Vector Machine with Linear Kernel (LinearSVM), and LightGBM Random Forest (LightRF) were used for Classification.
The top-performing model was LinearSVM with a test-set Balanced Accuracy of 0.960, followed by LightRF and GLM with balanced_accuracy of 0.921 and 0.888 respectively.
Generalized Linear Model (GLM), Support Vector Machine with Linear Kernel (LinearSVM), and LightGBM Random Forest (LightRF) were used for Classification.
The top-performing model was LinearSVM with a test-set Balanced Accuracy of 0.960, followed by LightRF and GLM with balanced_accuracy of 0.921 and 0.888 respectively.
Generalized Linear Model (GLM), Support Vector Machine with Linear Kernel (LinearSVM), and LightGBM Random Forest (LightRF) were used for Regression.
The top-performing model was LinearSVM with a test-set Rsq of 0.821, followed by GLM and LightRF with rsq of 0.817 and 0.661 respectively.
Generalized Linear Model (GLM), Support Vector Machine with Linear Kernel (LinearSVM), and LightGBM Random Forest (LightRF) were used for Regression.
The top-performing model was LinearSVM with a test-set Rsq of 0.821, followed by GLM and LightRF with rsq of 0.817 and 0.661 respectively.
2026-10-05 11:04:24 <1b>[0m<> Calibrating GLM resampled classification...<1b>[0m [calibrate]
<Resampled Classification Model>
GLM (Generalized Linear Model)
⟳ Tested using 3 independent folds.
⟋ Calibrated using Isotonic Regression with 5 independent folds.
<Resampled Classification Training Metrics (Pre => Post Calibration)>
Showing mean (sd) across resamples, Pre => Post calibration.
sensitivity: 0.99 (0.02) => 0.95 (0.08)
specificity: 0.99 (0.02) => 0.91 (0.09)
balanced_accuracy: 0.99 (0.02) => 0.93 (0.07)
ppv: 0.99 (0.02) => 0.92 (0.08)
npv: 0.99 (0.02) => 0.95 (0.07)
f1: 0.99 (0.02) => 0.93 (0.07)
accuracy: 0.99 (0.02) => 0.93 (0.07)
auc: 1.00 (3.7e-03) => 0.94 (0.05)
brier_score: 0.01 (0.01) => 0.05 (0.05)
<Resampled Classification Test Metrics (Pre => Post Calibration)>
Showing mean (sd) across resamples, Pre => Post calibration.
sensitivity: 0.88 (0.13) => 0.94 (0.13)
specificity: 0.90 (0.09) => 0.89 (0.20)
balanced_accuracy: 0.89 (0.09) => 0.91 (0.12)
ppv: 0.90 (0.10) => 0.92 (0.15)
npv: 0.89 (0.12) => 0.94 (0.12)
f1: 0.89 (0.10) => 0.92 (0.11)
accuracy: 0.89 (0.09) => 0.91 (0.12)
auc: 0.94 (0.06) => 0.92 (0.12)
brier_score: 0.10 (0.09) => 0.08 (0.11)
2026-10-05 11:04:29 <1b>[0m</> Calibration done.<1b>[0m [calibrate]
2026-10-05 11:04:29 <1b>[0m<> Calibrating CART resampled classification...<1b>[0m [calibrate]
<Resampled Classification Model>
CART (Classification and Regression Trees)
⚙ Tuned using exhaustive grid search.
⟳ Tested using 3 independent folds.
⟋ Calibrated using Isotonic Regression with 5 independent folds.
<Resampled Classification Training Metrics (Pre => Post Calibration)>
Showing mean (sd) across resamples, Pre => Post calibration.
sensitivity: 0.94 (0.06) => 0.92 (0.08)
specificity: 0.95 (0.06) => 0.92 (0.08)
balanced_accuracy: 0.95 (0.01) => 0.92 (0.02)
ppv: 0.95 (0.06) => 0.93 (0.07)
npv: 0.95 (0.05) => 0.93 (0.07)
f1: 0.94 (0.01) => 0.92 (0.02)
accuracy: 0.95 (0.01) => 0.92 (0.02)
auc: 0.95 (0.01) => 0.92 (0.02)
brier_score: 0.05 (0.01) => 0.07 (0.02)
<Resampled Classification Test Metrics (Pre => Post Calibration)>
Showing mean (sd) across resamples, Pre => Post calibration.
sensitivity: 0.92 (0.09) => 0.92 (0.14)
specificity: 0.92 (0.09) => 0.92 (0.14)
balanced_accuracy: 0.92 (0.01) => 0.92 (0.08)
ppv: 0.93 (0.08) => 0.94 (0.10)
npv: 0.93 (0.08) => 0.94 (0.11)
f1: 0.92 (0.02) => 0.92 (0.09)
accuracy: 0.92 (0.01) => 0.92 (0.08)
auc: 0.91 (0.03) => 0.92 (0.08)
brier_score: 0.07 (0.01) => 0.08 (0.06)
2026-10-05 11:04:33 <1b>[0m</> Calibration done.<1b>[0m [calibrate]
2026-10-05 11:04:33 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:04:33 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:04:33 <1b>[0mTraining set: 90 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:04:33 <1b>[0m Test set: 10 cases x 5 features.<1b>[0m [summarize_supervised]
2026-10-05 11:04:33 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:04:33 <1b>[0mPreprocessing...<1b>[0m [train]
2026-10-05 11:04:33 <1b>[0mTraining GLM Classification...<1b>[0m [train]
2026-10-05 11:04:33 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLM<1b>[0m (Generalized Linear Model)
<1b>[1;38;2;82;101;81m▣<1b>[0m Preprocessed using centering, scaling.
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 44 1
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 44
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.978
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.978
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.998
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.018
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTest Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 5 0
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 4
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.800
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.833
<1b>[22;38;2;128;128;128m Npv<1b>[0m 1.000
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.909
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.900
<1b>[22;38;2;128;128;128m Auc<1b>[0m 1.000
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.093
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:04:33 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.42 seconds.<1b>[0m [train]
rtemis Color System
<1b>[1m highlight_col<1b>[22m: <1b>[22;38;2;108;163;160m░<1b>[0m<1b>[22;38;2;108;163;160m░<1b>[0m<1b>[22;38;2;108;163;160m▓<1b>[0m<1b>[22;38;2;108;163;160m▓<1b>[0m<1b>[22;38;2;108;163;160m█<1b>[0m<1b>[22;38;2;108;163;160m█<1b>[0m<1b>[22;38;2;108;163;160m▓<1b>[0m<1b>[22;38;2;108;163;160m▓<1b>[0m<1b>[22;38;2;108;163;160m░<1b>[0m<1b>[22;38;2;108;163;160m░<1b>[0m
<1b>[1m col_warn<1b>[22m: <1b>[22;38;2;240;137;4m░<1b>[0m<1b>[22;38;2;240;137;4m░<1b>[0m<1b>[22;38;2;240;137;4m▓<1b>[0m<1b>[22;38;2;240;137;4m▓<1b>[0m<1b>[22;38;2;240;137;4m█<1b>[0m<1b>[22;38;2;240;137;4m█<1b>[0m<1b>[22;38;2;240;137;4m▓<1b>[0m<1b>[22;38;2;240;137;4m▓<1b>[0m<1b>[22;38;2;240;137;4m░<1b>[0m<1b>[22;38;2;240;137;4m░<1b>[0m
<1b>[1m col_error<1b>[22m: <1b>[22;38;2;234;56;74m░<1b>[0m<1b>[22;38;2;234;56;74m░<1b>[0m<1b>[22;38;2;234;56;74m▓<1b>[0m<1b>[22;38;2;234;56;74m▓<1b>[0m<1b>[22;38;2;234;56;74m█<1b>[0m<1b>[22;38;2;234;56;74m█<1b>[0m<1b>[22;38;2;234;56;74m▓<1b>[0m<1b>[22;38;2;234;56;74m▓<1b>[0m<1b>[22;38;2;234;56;74m░<1b>[0m<1b>[22;38;2;234;56;74m░<1b>[0m
<1b>[1m col_success<1b>[22m: <1b>[22;38;2;0;204;143m░<1b>[0m<1b>[22;38;2;0;204;143m░<1b>[0m<1b>[22;38;2;0;204;143m▓<1b>[0m<1b>[22;38;2;0;204;143m▓<1b>[0m<1b>[22;38;2;0;204;143m█<1b>[0m<1b>[22;38;2;0;204;143m█<1b>[0m<1b>[22;38;2;0;204;143m▓<1b>[0m<1b>[22;38;2;0;204;143m▓<1b>[0m<1b>[22;38;2;0;204;143m░<1b>[0m<1b>[22;38;2;0;204;143m░<1b>[0m
<1b>[1m col_preprocessor<1b>[22m: <1b>[22;38;2;82;101;81m░<1b>[0m<1b>[22;38;2;82;101;81m░<1b>[0m<1b>[22;38;2;82;101;81m▓<1b>[0m<1b>[22;38;2;82;101;81m▓<1b>[0m<1b>[22;38;2;82;101;81m█<1b>[0m<1b>[22;38;2;82;101;81m█<1b>[0m<1b>[22;38;2;82;101;81m▓<1b>[0m<1b>[22;38;2;82;101;81m▓<1b>[0m<1b>[22;38;2;82;101;81m░<1b>[0m<1b>[22;38;2;82;101;81m░<1b>[0m
<1b>[1m col_decom<1b>[22m: <1b>[22;38;2;15;106;102m░<1b>[0m<1b>[22;38;2;15;106;102m░<1b>[0m<1b>[22;38;2;15;106;102m▓<1b>[0m<1b>[22;38;2;15;106;102m▓<1b>[0m<1b>[22;38;2;15;106;102m█<1b>[0m<1b>[22;38;2;15;106;102m█<1b>[0m<1b>[22;38;2;15;106;102m▓<1b>[0m<1b>[22;38;2;15;106;102m▓<1b>[0m<1b>[22;38;2;15;106;102m░<1b>[0m<1b>[22;38;2;15;106;102m░<1b>[0m
<1b>[1m col_outer<1b>[22m: <1b>[22;38;2;190;46;95m░<1b>[0m<1b>[22;38;2;190;46;95m░<1b>[0m<1b>[22;38;2;190;46;95m▓<1b>[0m<1b>[22;38;2;190;46;95m▓<1b>[0m<1b>[22;38;2;190;46;95m█<1b>[0m<1b>[22;38;2;190;46;95m█<1b>[0m<1b>[22;38;2;190;46;95m▓<1b>[0m<1b>[22;38;2;190;46;95m▓<1b>[0m<1b>[22;38;2;190;46;95m░<1b>[0m<1b>[22;38;2;190;46;95m░<1b>[0m
<1b>[1m col_tuner<1b>[22m: <1b>[22;38;2;243;132;255m░<1b>[0m<1b>[22;38;2;243;132;255m░<1b>[0m<1b>[22;38;2;243;132;255m▓<1b>[0m<1b>[22;38;2;243;132;255m▓<1b>[0m<1b>[22;38;2;243;132;255m█<1b>[0m<1b>[22;38;2;243;132;255m█<1b>[0m<1b>[22;38;2;243;132;255m▓<1b>[0m<1b>[22;38;2;243;132;255m▓<1b>[0m<1b>[22;38;2;243;132;255m░<1b>[0m<1b>[22;38;2;243;132;255m░<1b>[0m
<1b>[1m col_info<1b>[22m: <1b>[22;38;2;70;109;150m░<1b>[0m<1b>[22;38;2;70;109;150m░<1b>[0m<1b>[22;38;2;70;109;150m▓<1b>[0m<1b>[22;38;2;70;109;150m▓<1b>[0m<1b>[22;38;2;70;109;150m█<1b>[0m<1b>[22;38;2;70;109;150m█<1b>[0m<1b>[22;38;2;70;109;150m▓<1b>[0m<1b>[22;38;2;70;109;150m▓<1b>[0m<1b>[22;38;2;70;109;150m░<1b>[0m<1b>[22;38;2;70;109;150m░<1b>[0m
warning: Failed to inspect Python interpreter from search path at `/usr/sbin/pypy`
cause: Can't use Python at `/usr/sbin/pypy`
cause: Python executable does not support `-I` flag. Please use Python 3.6 or newer.
Group counts:
group
Low NS High
1 98 1
2026-10-05 11:04:59 <1b>[0m▶<1b>[0m [massGLM]
2026-10-05 11:04:59 <1b>[0mScaling and centering 40 numeric features...<1b>[0m [preprocess]
2026-10-05 11:04:59 <1b>[0mPreprocessing done.<1b>[0m [preprocess]
2026-10-05 11:04:59 <1b>[0mFitting 40 GLMs of family gaussian with 2 predictors each...<1b>[0m [massGLM]
2026-10-05 11:04:59 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.54 seconds.<1b>[0m [massGLM]
2026-10-05 11:04:59 <1b>[0mPlotting coefficients for x1 x 40 outcomes.<1b>[0m [`plot.rtemis::MassGLM`]
Group counts:
group
Low NS High
1 37 2
2026-10-05 11:05:00 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:05:00 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:05:00 <1b>[0mTraining set: 100 cases x 3 features.<1b>[0m [summarize_supervised]
2026-10-05 11:05:00 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:05:00 <1b>[0mTraining GLM Regression...<1b>[0m [train]
2026-10-05 11:05:00 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mRegression<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLM<1b>[0m (Generalized Linear Model)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Regression Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m MAE<1b>[22m: 0.84
<1b>[1m MSE<1b>[22m: 1.04
<1b>[1m RMSE<1b>[22m: 1.02
<1b>[1m R²<1b>[22m: 0.69
2026-10-05 11:05:00 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.21 seconds.<1b>[0m [train]
2026-10-05 11:05:01 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:05:01 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:05:01 <1b>[0mTraining set: 100 cases x 4 features.<1b>[0m [summarize_supervised]
2026-10-05 11:05:01 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 }<1b>[0m [get_n_workers]
2026-10-05 11:05:01 <1b>[0mTraining GLM Classification...<1b>[0m [train]
2026-10-05 11:05:01 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mClassification<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1;38;2;108;163;160mGLM<1b>[0m (Generalized Linear Model)
<1b>[22;38;2;108;163;160m<<1b>[0m<1b>[1mTraining Classification Metrics<1b>[0m<1b>[22;38;2;108;163;160m><1b>[0m
<1b>[1m Predicted<1b>[22m
<1b>[1m Reference<1b>[22m <1b>[1;38;2;108;163;160mvirginica <1b>[0m<1b>[1;38;2;108;163;160mversicolor <1b>[0m
<1b>[1;38;2;108;163;160m virginica<1b>[0m 49 1
<1b>[1;38;2;108;163;160m versicolor<1b>[0m 1 49
<1b>[1;38;2;108;163;160mOverall <1b>[0m
<1b>[22;38;2;128;128;128m Sensitivity<1b>[0m 0.980
<1b>[22;38;2;128;128;128m Specificity<1b>[0m 0.980
<1b>[22;38;2;128;128;128m Balanced Accuracy<1b>[0m 0.980
<1b>[22;38;2;128;128;128m Ppv<1b>[0m 0.980
<1b>[22;38;2;128;128;128m Npv<1b>[0m 0.980
<1b>[22;38;2;128;128;128m F1<1b>[0m 0.980
<1b>[22;38;2;128;128;128m Accuracy<1b>[0m 0.980
<1b>[22;38;2;128;128;128m Auc<1b>[0m 0.997
<1b>[22;38;2;128;128;128m Brier Score<1b>[0m 0.019
Positive Class <1b>[1;38;2;108;163;160mvirginica<1b>[0m
2026-10-05 11:05:01 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 0.13 seconds.<1b>[0m [train]
2026-10-05 11:05:01 <1b>[0mChecking data is ready for training... ✔ [check_supervised]
2026-10-05 11:05:01 <1b>[0m▶<1b>[0m [train]
2026-10-05 11:05:01 <1b>[0mTraining set: 100 cases x 3 features.<1b>[0m [summarize_supervised]
2026-10-05 11:05:01 <1b>[0m// Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: c(`_R_CHECK_LIMIT_CORES_` = 1) }<1b>[0m [get_n_workers]
2026-10-05 11:05:01 <1b>[0m<> Training GLM Regression using 3 independent folds...<1b>[0m [train]
2026-10-05 11:05:01 <1b>[0mInput contains more than one column; stratifying on last.<1b>[0m [resample]
2026-10-05 11:05:02 <1b>[0m</> Outer resampling done.<1b>[0m [train]
<Resampled Regression Model>
GLM (Generalized Linear Model)
⟳ Tested using 3 independent folds.
<Resampled Regression Training Metrics>
Showing mean (sd) across resamples.
MAE: 0.821 (0.012)
MSE: 1.008 (0.053)
RMSE: 1.004 (0.026)
R²: 0.697 (0.020)
<Resampled Regression Test Metrics>
Showing mean (sd) across resamples.
MAE: 0.901 (0.029)
MSE: 1.193 (0.074)
RMSE: 1.092 (0.034)
R²: 0.637 (0.047)
2026-10-05 11:05:02 <1b>[0m<1b>[1;38;2;15;106;102m✓<1b>[0m Done in 1.04 seconds.<1b>[0m [train]
[ FAIL 1 | WARN 4 | SKIP 4 | PASS 356 ]
══ Skipped tests (4) ═══════════════════════════════════════════════════════════
• For local testing only; requires CSV file (3): 'test_ClusterConfig.R:19:3',
'test_DecomposeConfig.R:19:3', 'test_SuperConfig.R:48:3'
• empty test (1):
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test_Clustering.R:96:3'): cluster_DBSCAN() succeeds ─────────────────
Error: approx must be a single, finite, nonnegative number.
Backtrace:
▆
1. └─rtemis::cluster(...) at test_Clustering.R:96:3
2. └─rtemis:::cluster_(config = config, x = x, verbosity = verbosity)
3. ├─S7::S7_dispatch()
4. └─rtemis (local) `method(cluster_, rtemis::DBSCANConfig)`(...)
5. └─dbscan::dbscan(...)
6. └─dbscan:::.validate_nonnegative_scalar(extra$approx %||% 0, "approx")
[ FAIL 1 | WARN 4 | SKIP 4 | PASS 356 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc
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