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.
Book on mlr3: https://mlr3book.mlr-org.com
Use cases and examples gallery: https://mlr3gallery.mlr-org.com
Cheat Sheets: https://github.com/mlr-org/mlr3cheatsheets
Preprocessing and machine learning pipelines: mlr3pipelines
Analysis of benchmark experiments: mlr3benchmark
More classification and regression tasks: mlr3data
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
Parallelization framework: future
Progress bars: progressr
"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.
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.
Martin Binder email@example.com