Package website: release | dev

This packages provides essential learners for mlr3, maintained by the mlr-org team. We will most likely not add new learners to this package. See section “More Learners” below for more information.


# CRAN version:

# Development version:

If you also want to install all packages of the connected learners, set dependencies = TRUE:

# CRAN version:
install.packages("mlr3learners", dependencies = TRUE)

# Development version:
remotes::install_github("mlr-org/mlr3learners", dependencies = TRUE)

Classification Learners

ID Learner Package
classif.cv_glmnet Penalized Logistic Regression glmnet
classif.glmnet Penalized Logistic Regression glmnet
classif.kknn k-Nearest Neighbors kknn
classif.lda LDA MASS
classif.log_reg Logistic Regression stats
classif.multinom Multinomial log-linear model nnet
classif.naive_bayes Naive Bayes e1071
classif.qda QDA MASS
classif.ranger Random Forest ranger
classif.svm SVM e1071
classif.xgboost Gradient Boosting xgboost

Regression Learners

ID Learner Package
regr.cv_glmnet Penalized Linear Regression glmnet
regr.glmnet Penalized Linear Regression glmnet
regr.kknn k-Nearest Neighbors kknn Kriging DiceKriging
regr.lm Linear Regression stats
regr.ranger Random Forest ranger
regr.svm SVM e1071
regr.xgboost Gradient Boosting xgboost

More Learners

Learners from other packages live in the mlr3learners organization and are possibly maintained by people outside the mlr-org team.

👉 Table of all additional learners

Requesting/Adding Additional Learners

Follow these steps to add/request a new learner.

  1. Open an issue in mlr3learners following the issue template. (If you don’t want to add the learner yourself, you are done here. We might take your vote into consideration but can’t promise when your requested learner will be available.)
  2. Fork the mlr3learners.template repo and adjust the template to match your learner. Essentially, replace the placeholders like <package> with the respective terms for your learner. More detailed instructions including FAQ are given in section “Adding new Learners” of the mlr3book.
  3. When you are done, request a review for the learner. Make sure that you have checked on all points of this checklist before requesting a review.
  4. After approval, transfer the learner to the mlr3learners organization. To do so, please first request an invitation from @pat-s / @be-marc to be added to the mlr3learners organization. Once transferred, you will get access rights to the learner repository to finalize and maintain it.
  5. Congrats! Your learner has been successfully added to the mlr3 ecosystem. Now the last step is to add the learner to {mlr3learners.drat}. This makes is possible to install the learner via install.packages() without the need to submit the learner to CRAN (none of the custom learners live on CRAN).

Resources for adding a new learner (summary)

Last, thanks for contributing to the mlr3 ecosystem! We would be very happy if you keep maintaining the learner against upstream changes :)