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mlr3learners 0.8.0

• fix: Hyperparameter set of lrn("classif.ranger") and lrn("regr.ranger"). Remove alpha and minprop hyperparameter. Remove default of respect.unordered.factors. Change lower bound of max_depth from 0 to 1. Remove se.method from lrn("classif.ranger").
• feat: use base_margin in xgboost learners (#205).
• fix: validation for learner lrn("regr.xgboost") now works properly. Previously the training data was used.
• feat: add weights for logistic regression again, which were incorrectly removed in a previous release (#265).
• BREAKING CHANGE: When using internal tuning for xgboost learners, the eval_metric must now be set. This achieves that one needs to make the conscious decision which performance metric to use for early stopping.
• BREAKING CHANGE: Change xgboost default nrounds from 1 to 1000.

mlr3learners 0.7.0

• feat: LearnerClassifXgboost and LearnerRegrXgboost now support internal tuning and validation. This now also works in conjunction with mlr3pipelines.

mlr3learners 0.6.0

• Adaption to new paradox version 1.0.0.

mlr3learners 0.5.8

• Adaption to memory optimization in mlr3 0.17.1.

mlr3learners 0.5.7

• Added labels to learners.
• Added formula argument to nnet learner and support feature type "integer".
• Added min.bucket parameter to classif.ranger and regr.ranger.

mlr3learners 0.5.6

• Enable new early stopping mechanism for xgboost.
• Improved documentation.
• fix: unloading mlr3learners removes learners from dictionary.

mlr3learners 0.5.4

• Added regr.nnet learner.
• Removed the option to use weights in classif.log_reg.
• Added default_values() function for ranger and svm learners.
• Improved documentation.

mlr3learners 0.5.3

• Survival learners have been moved to mlr3extralearners (maintained on Github): https://github.com/mlr-org/mlr3extralearners

mlr3learners 0.5.2

• Most learners now reorder the columns in the predict task according to the order of columns in the training task.
• Removed workaround for old mlr3 versions.

mlr3learners 0.5.1

• eval_metric() is now explicitly set for xgboost learners to silence a deprecation warning.
• Improved how the added hyperparameter mtry.ratio is converted to mtry to simplify tuning.
• Multiple updates to hyperparameter sets.

mlr3learners 0.5.0

• Fixed the internal encoding of the positive class for classification learners based on glm and glmnet (#199). While predictions in previous versions were correct, the estimated coefficients had the wrong sign.
• Reworked handling of lambda and s for glmnet learners (#197).
• Learners based on glmnet now support to extract selected features (#200).
• Learners based on kknn now raise an exception if k >= n (#191).
• Learners based on ranger now come with the virtual hyperparameter mtry.ratio to set the hyperparameter mtry based on the proportion of features to use.
• Multiple learners now support the extraction of the log-likelihood (via method $loglik()), allowing to calculate measures like AIC or BIC in mlr3 (#182).

mlr3learners 0.4.5

• Fixed SVM learners for new release of package e1071.

mlr3learners 0.4.4

• Changed hyperparameters of all learners to make them run sequentially in their defaults. The new function set_threads() in mlr3 provides a generic way to set the respective hyperparameter to the desired number of parallel threads.
• Added survival:aft objective to surv.xgboost
• Removed hyperparameter predict.all from ranger learners (#172).

mlr3learners 0.4.3

• Fixed stochastic test failures on solaris.
• Fixed surv.ranger, c.f. https://github.com/mlr-org/mlr3proba/issues/165.
• Added classif.nnet learner (moved from mlr3extralearners).

mlr3learners 0.4.2

• Fixed a bug in the survival random forest LearnerSurvRanger.

mlr3learners 0.4.1

• Disabled some glmnet tests on solaris.
• Removed dependency on orphaned package bibtex.

mlr3learners 0.4.0

• Fixed a potential label switch in classif.glmnet and classif.cv_glmnet with predict_type set to "prob" (#155).
• Fixed learners from package glmnet to be more robust if the order of features has changed between train and predict.

mlr3learners 0.3.0

• The $model slot of the {kknn} learner now returns a list containing some information which is being used during the predict step. Before, the slot was empty because there is no training step for kknn.
• Compact in-memory representation of R6 objects to save space when saving mlr3 objects via saveRDS(), serialize() etc.
• glmnet learners: penalty.factor is a vector param, not a ParamDbl (#141)
• glmnet: Add params mxitnr and epsnr from glmnet v4.0 update
• Add learner surv.glmnet (#130)
• Suggest package mlr3proba (#144)
• Add learner surv.xgboost (#135)
• Add learner surv.ranger (#134)

mlr3learners 0.2.0

• Split glmnet learner into cv_glmnet and glmnet (#99)
• glmnet learners: Add predict.gamma and newoffset arg (#98)
• We now test that all learners can be constructed without parameters.
• A new custom “Paramtest” which lives inst/paramtest was added. This test checks against the arguments of the upstream train & predict functions and ensures that all parameters are implemented in the respective mlr3 learner (#96).
• A lot missing parameters were added to learners. See #96 for a complete list.
• Add parameter interaction_constraints to {xgboost} learners (#97).

mlr3learners 0.1.6.9000

• Added learner classif.multinom from package nnet.
• Learners regr.lm and classif.log_reg now ignore the global option "contrasts".
• Add vignette additional-learners.Rmd listing all mlr3 custom learners
• Move Learner*Glmnet to Learner*CVGlmnet and add Learner*Glmnet (without internal tuning) (#90)

XGBoost

• Add parameter interaction_constraints (#95)

mlr3learners 0.1.6

• Added missing feature type logical() to multiple learners.

mlr3learners 0.1.5

• Added parameter and parameter dependencies to regr.glmnet, regr.km, regr.ranger, regr.svm, regr.xgboost, classif.glmnet, classif.lda, classif.naivebayes, classif.qda, classif.ranger and classif.svm.
• glmnet: Added relax parameter (v3.0)
• xgboost: Updated parameters for v0.90.0.2

mlr3learners 0.1.4

• Fixed a bug in *.xgboost and *.svm which was triggered if columns were reordered between $train() and $predict().

mlr3learners 0.1.3

• Changes to work with new mlr3::Learner API.

• Improved documentation.

• Added references.

• add new parameters of xgboost version 0.90.2

• add parameter dependencies for xgboost

mlr3learners 0.1.2

• Maintenance release.

mlr3learners 0.1.1

• Initial upload to CRAN.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.