This packages provides essential learners for mlr3, maintained by the mlr-org team. We will most likely not add new learners to this package.

Other learners live in the mlr3learners organization and are possibly maintained by people outside the mlr-org team. There is a wiki page listing all currently available custom learners. See below for instructions on how to add a new learner.

Installation

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

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

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

Classification Learners

ID Learner Package
classif.glmnet Penalized Logistic Regression glmnet
classif.kknn k-Nearest Neighbors kknn
classif.lda LDA MASS
classif.log_reg Logistic Regression stats
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.glmnet Penalized Linear Regression glmnet
regr.kknn k-Nearest Neighbors kknn
regr.km Kriging DiceKriging
regr.lm Linear Regression stats
regr.ranger Random Forest ranger
regr.svm SVM e1071
regr.xgboost Gradient Boosting xgboost

Requesting/Adding additional learners

Please follow these steps to add/request a new learner. Steps 2-6 are only needed if you want to add a learner yourself.

  1. Open an issue in mlr3learners following the issue template.

  2. Fork the mlr3learnertemplate repo and adjust the template to your needs. Follow the instructions given in the mlr3book to get started.

  3. When you are somewhat done, request to add your learner to the mlr3learners organization by transfering your repository to the mlr3learners organization. To do so, please request an invation to be added to the mlr3learners organization. Add your learner to the matching section in the mlr3learners wiki. Once transfered, you will get access rights to your repository to finalize the learner.

  4. Once you are happy with the status of the learner, request a review in the issue from @mllg, @berndbischl, @pat-s or @be-marc

  5. Congrats! Your learner has been successfully added to the mlr3 ecosystem. We would be happy it you also add your learner to {mlr3learners.drat} to simplify installation. In addition, we would also be grateful if you keep maintaining it against upstream changes :)