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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

mlr3 extensions

Suggested packages

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.

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

Author

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

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