Ecosystem

The mlr3 ecosystem is a collection of R packages for machine learning. The base package mlr3 only provides the basic building blocks for machine learning. The extensions packages extent mlr3 with functionality for additional task types, learning algorithms, tuning algorithms, feature selection strategies, visualizations or preprocessing capabilities. The packages are listed bellow with a short description. For more information about the packages, check out their respective homepages.

The dot next to the package name indicates the lifecycle stage.

Graph of Extension Packages

If you use our packages in your research, please cite our articles on mlr3 (Lang et al. 2019), mlr3proba (Sonabend et al. 2021) or mlr3pipelines (Binder et al. 2021). To get the citation information of other packages, call

citation("[package]")

Core

mlr3

mlr3verse

Optimization

mlr3tuning

mlr3tuningspaces

mlr3hyperband

mlr3mbo

miesmuschel

mlr3automl

Feature Selection

mlr3filters

mlr3fselect

Data

mlr3db

mlr3oml

mlr3data

Learners

mlr3learners

mlr3extralearners

mlr3keras

Visualization

mlr3viz

Pipelines

mlr3pipelines

Tasks

mlr3spatiotempcv

mlr3cluster

mlr3proba

mlr3spatial

mlr3ordinal

mlr3multioutput

mlr3temporal

mlr3multilabel

mlr3fda

Utilities

bbotk

paradox

mlr3misc

mlr3measures

mlr3benchmark

mlr3fairness

mlr3batchmark

References

Binder, Martin, Florian Pfisterer, Michel Lang, Lennart Schneider, Lars Kotthoff, and Bernd Bischl. 2021. mlr3pipelines - Flexible Machine Learning Pipelines in R.” Journal of Machine Learning Research 22 (184): 1–7. https://jmlr.org/papers/v22/21-0281.html.
Lang, Michel, Martin Binder, Jakob Richter, Patrick Schratz, Florian Pfisterer, Stefan Coors, Quay Au, Giuseppe Casalicchio, Lars Kotthoff, and Bernd Bischl. 2019. mlr3: A Modern Object-Oriented Machine Learning Framework in R.” Journal of Open Source Software, December. https://doi.org/10.21105/joss.01903.
Sonabend, Raphael, Franz J Király, Andreas Bender, Bernd Bischl, and Michel Lang. 2021. mlr3proba: An R Package for Machine Learning in Survival Analysis.” Bioinformatics, February. https://doi.org/10.1093/bioinformatics/btab039.