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Recommended Learners for 'mlr3'. Extends 'mlr3' with interfaces to essential machine learning packages on CRAN. This includes, but is not limited to: (penalized) linear and logistic regression, linear and quadratic discriminant analysis, k-nearest neighbors, naive Bayes, support vector machines, and gradient boosting.
Version: | 0.8.0 |
Depends: | mlr3 (≥ 0.21.1), R (≥ 3.1.0) |
Imports: | checkmate, data.table, mlr3misc (≥ 0.9.4), paradox (≥ 1.0.0), R6 |
Suggests: | DiceKriging, e1071, glmnet, kknn, knitr, lgr, MASS, nnet, pracma, ranger, rgenoud, rmarkdown, testthat (≥ 3.0.0), xgboost (≥ 1.6.0) |
Published: | 2024-10-25 |
DOI: | 10.32614/CRAN.package.mlr3learners |
Author: | Michel Lang [aut], Quay Au [aut], Stefan Coors [aut], Patrick Schratz [aut], Marc Becker [cre, aut] |
Maintainer: | Marc Becker <marcbecker at posteo.de> |
BugReports: | https://github.com/mlr-org/mlr3learners/issues |
License: | LGPL-3 |
URL: | https://mlr3learners.mlr-org.com, https://github.com/mlr-org/mlr3learners |
NeedsCompilation: | no |
Materials: | README NEWS |
CRAN checks: | mlr3learners results |
Reference manual: | mlr3learners.pdf |
Package source: | mlr3learners_0.8.0.tar.gz |
Windows binaries: | r-devel: mlr3learners_0.8.0.zip, r-release: mlr3learners_0.8.0.zip, r-oldrel: mlr3learners_0.8.0.zip |
macOS binaries: | r-release (arm64): mlr3learners_0.8.0.tgz, r-oldrel (arm64): mlr3learners_0.8.0.tgz, r-release (x86_64): mlr3learners_0.8.0.tgz, r-oldrel (x86_64): mlr3learners_0.8.0.tgz |
Old sources: | mlr3learners archive |
Reverse depends: | GenericML, mlr3superlearner |
Reverse imports: | DoubleML, highMLR, mlr3fairness, mlr3shiny, mlr3verse, sense, SIAMCAT, spFSR |
Reverse suggests: | counterfactuals, cpi, explainer, MantaID, mcboost, miesmuschel, mlr3benchmark, mlr3filters, mlr3fselect, mlr3hyperband, mlr3mbo, mlr3pipelines, mlr3spatial, mlr3summary, mlr3tuning, mlr3tuningspaces, mlr3viz, mlrintermbo, paradox, vetiver, vivid |
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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.