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.

• Packages with a green dot are stable.
• Experimental packages are marked with an orange dot .
• Planned packages are marked with a red dot .

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

Basic building blocks for machine learning.

mlr3verse

Meta-package intended to simplify both installation and loading of packages from the mlr3 ecosystem.

mlr3pipelines

Dataflow programming toolkit.

Learners

mlr3learners

Essential learners for mlr3, maintained by the mlr-org team.

mlr3extralearners

Extra learners for mlr3, implemented by the community.

mlr3torch

Deep learning with Torch.

Optimization

mlr3tuning

Hyperparameter tuning for mlr3 learners.

mlr3tuningspaces

Collection of search spaces for hyperparameter tuning.

mlr3hyperband

Successive halving and hyperband tuner for mlr3tuning.

mlr3mbo

Model-based optimization for mlr3tuning.

miesmuschel

Flexible mixed integer evolutionary strategies.

bbotk

Black-box optimization toolkit.

mlr3automl

Automated machine learning.

Tasks and Datatypes

mlr3spatiotempcv

Spatiotemporal resampling and visualization methods.

mlr3cluster

Cluster analysis.

mlr3proba

Probabilistic predictions.

mlr3spatial

Spatial data backends and prediction functions.

mlr3fairness

Fairness in Machine Learning.

mlr3temporal

Time series analysis.

mlr3fda

Functional Data Analysis.

Feature Selection

mlr3filters

Filter Feature Selection.

mlr3fselect

Wrapper Feature Selection.

Data

mlr3db

Data backend to transparently work with databases.

mlr3oml

Connector to OpenML.

mlr3data

Data sets and tasks.

Analysis

mlr3viz

Visualizations for tasks, predictions, resample results and benchmarks.

mlr3benchmark

Analysis and tools for benchmarking.

Utilities

paradox

Universal parameter space description and tools.

mlr3misc

Miscellaneous helper functions for mlr3.

mlr3measures

Performance measures for supervised learning.

Parallelization

mlr3batchmark

Connector between mlr3 and batchtools.

rush

Asynchronous parallelization.

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.