Description

Efficient, object-oriented programming on the building blocks of machine learning. Provides 'R6' objects for tasks, learners, resamplings, and measures. The package is geared towards scalability and larger datasets by supporting parallelization and out-of-memory data-backends like databases. While 'mlr3' focuses on the core computational operations, add-on packages provide additional functionality.

Learn mlr3

• Book on mlr3: https://mlr3book.mlr-org.com

• Use cases and examples gallery: https://mlr3gallery.mlr-org.com

• Cheat Sheets: https://cheatsheets.mlr-org.com

mlr3 extensions

• Preprocessing and machine learning pipelines: mlr3pipelines

• Analysis of benchmark experiments: mlr3benchmark

• More classification and regression tasks: mlr3data

• Connector to OpenML: mlr3oml

• Solid selection of good classification and regression learners: mlr3learners

• Even more learners: https://github.com/mlr-org/mlr3extralearners

• Tuning of hyperparameters: mlr3tuning

• Hyperband tuner: mlr3hyperband

• Visualizations for many mlr3 objects: mlr3viz

• Survival analysis and probabilistic regression: mlr3proba

• Cluster analysis: mlr3cluster

• Feature selection filters: mlr3filters

• Feature selection wrappers: mlr3fselect

• Interface to real (out-of-memory) data bases: mlr3db

• Performance measures as plain functions: mlr3measures

Suggested packages

• Parallelization framework: future

• Progress bars: progressr

• Encapsulated evaluation: evaluate, callr (external process)

Package Options

• "mlr3.debug": If set to TRUE, parallelization via future is disabled to simplify debugging and provide more concise tracebacks. Note that results computed with debug mode enabled use a different seeding mechanism and are not reproducible.

• "mlr3.allow_utf8_names": If set to TRUE, checks on the feature names are relaxed, allowing non-ascii characters in column names. This is an experimental and temporal option to pave the way for text analysis, and will likely be removed in a future version of the package. analysis.

Author(s)

Maintainer: Michel Lang michellang@gmail.com (ORCID)

Authors:

• Bernd Bischl bernd_bischl@gmx.net (ORCID)

• Jakob Richter jakob1richter@gmail.com (ORCID)

• Patrick Schratz patrick.schratz@gmail.com (ORCID)

• Martin Binder mlr.developer@mb706.com

Other contributors:

• Giuseppe Casalicchio giuseppe.casalicchio@stat.uni-muenchen.de (ORCID) [contributor]

• Stefan Coors mail@stefancoors.de (ORCID) [contributor]

• Quay Au quayau@gmail.com (ORCID) [contributor]

• Marc Becker marcbecker@posteo.de (ORCID) [contributor]

References

Lang M, Binder M, Richter J, Schratz P, Pfisterer F, Coors S, Au Q, Casalicchio G, Kotthoff L, Bischl B (2019). “mlr3: A modern object-oriented machine learning framework in R.” Journal of Open Source Software. doi: 10.21105/joss.01903, https://joss.theoj.org/papers/10.21105/joss.01903.

See Also

Useful links:

• https://mlr3.mlr-org.com

• https://github.com/mlr-org/mlr3

• Report bugs at https://github.com/mlr-org/mlr3/issues