Package: mlr3learners 0.15.0

Marc Becker

mlr3learners: Recommended Learners for 'mlr3'

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.

Authors:Michel Lang [aut], Quay Au [aut], Stefan Coors [aut], Patrick Schratz [aut], Marc Becker [cre, aut], John Zobolas [aut], Alexander Winterstetter [ctb], Toby Hocking [ctb]

mlr3learners_0.15.0.tar.gz
mlr3learners_0.15.0.tar.gz(r-4.7-arm64)mlr3learners_0.15.0.tar.gz(r-4.7-x86_64)mlr3learners_0.15.0.tar.gz(r-4.6-arm64)mlr3learners_0.15.0.tar.gz(r-4.6-x86_64)
mlr3learners_0.15.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
mlr3learners/json (API)
NEWS

# Install 'mlr3learners' in R:
install.packages('mlr3learners', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/mlr-org/mlr3learners/issues

Pkgdown/docs site:https://mlr3learners.mlr-org.com

On CRAN:

Conda:

7.91 score 32 packages 2.1k scripts 28k downloads 1 mentions 21 exports 25 dependencies

Last updated from:16550ab9e1. Checks:6 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK201
linux-devel-x86_64OK211
source / vignettesOK190
linux-release-arm64OK219
linux-release-x86_64OK204
wasm-releaseOK125

Exports:LearnerClassifCVGlmnetLearnerClassifGlmnetLearnerClassifKKNNLearnerClassifLDALearnerClassifLogRegLearnerClassifMultinomLearnerClassifNaiveBayesLearnerClassifNnetLearnerClassifQDALearnerClassifRangerLearnerClassifSVMLearnerClassifXgboostLearnerRegrCVGlmnetLearnerRegrGlmnetLearnerRegrKKNNLearnerRegrKMLearnerRegrLMLearnerRegrNnetLearnerRegrRangerLearnerRegrSVMLearnerRegrXgboost

Dependencies:backportscheckmateclicodetoolsdata.tabledigestevaluatefuturefuture.applyglobalslgrlistenvmiraimlbenchmlr3mlr3measuresmlr3miscnanonextpalmerpenguinsparadoxparallellyPRROCR6rlanguuid

Readme and manuals

Help Manual

Help pageTopics
mlr3learners: Recommended Learners for 'mlr3'mlr3learners-package mlr3learners
GLM with Elastic Net Regularization Classification LearnerLearnerClassifCVGlmnet mlr_learners_classif.cv_glmnet
GLM with Elastic Net Regularization Classification LearnerLearnerClassifGlmnet mlr_learners_classif.glmnet
k-Nearest-Neighbor Classification LearnerLearnerClassifKKNN mlr_learners_classif.kknn
Linear Discriminant Analysis Classification LearnerLearnerClassifLDA mlr_learners_classif.lda
Logistic Regression Classification LearnerLearnerClassifLogReg mlr_learners_classif.log_reg
Multinomial log-linear learner via neural networksLearnerClassifMultinom mlr_learners_classif.multinom
Naive Bayes Classification LearnerLearnerClassifNaiveBayes mlr_learners_classif.naive_bayes
Classification Neural Network LearnerLearnerClassifNnet mlr_learners_classif.nnet
Quadratic Discriminant Analysis Classification LearnerLearnerClassifQDA mlr_learners_classif.qda
Ranger Classification LearnerLearnerClassifRanger mlr_learners_classif.ranger
Support Vector MachineLearnerClassifSVM mlr_learners_classif.svm
Extreme Gradient Boosting Classification LearnerLearnerClassifXgboost mlr_learners_classif.xgboost
GLM with Elastic Net Regularization Regression LearnerLearnerRegrCVGlmnet mlr_learners_regr.cv_glmnet
GLM with Elastic Net Regularization Regression LearnerLearnerRegrGlmnet mlr_learners_regr.glmnet
k-Nearest-Neighbor Regression LearnerLearnerRegrKKNN mlr_learners_regr.kknn
Kriging Regression LearnerLearnerRegrKM mlr_learners_regr.km
Linear Model Regression LearnerLearnerRegrLM mlr_learners_regr.lm
Neural Network Regression LearnerLearnerRegrNnet mlr_learners_regr.nnet
Ranger Regression LearnerLearnerRegrRanger mlr_learners_regr.ranger
Support Vector MachineLearnerRegrSVM mlr_learners_regr.svm
Extreme Gradient Boosting Regression LearnerLearnerRegrXgboost mlr_learners_regr.xgboost