Machine Learning in R - Next Generation

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.


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Reference manual

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install.packages("mlr3")

0.3.0 by Michel Lang, a month ago


https://mlr3.mlr-org.com, https://github.com/mlr-org/mlr3


Report a bug at https://github.com/mlr-org/mlr3/issues


Browse source code at https://github.com/cran/mlr3


Authors: Michel Lang [cre, aut] , Bernd Bischl [aut] , Jakob Richter [aut] , Patrick Schratz [aut] , Giuseppe Casalicchio [ctb] , Stefan Coors [ctb] , Quay Au [ctb] , Martin Binder [aut]


Documentation:   PDF Manual  


Task views: Machine Learning & Statistical Learning


LGPL-3 license


Imports R6, backports, checkmate, data.table, digest, future.apply, lgr, mlbench, mlr3measures, mlr3misc, paradox, uuid

Suggests Matrix, bibtex, callr, datasets, evaluate, future, future.callr, mlr3data, progressr, rpart, testthat


Imported by mlr3db, mlr3filters, mlr3learners, mlr3pipelines, mlr3proba, mlr3shiny, mlr3tuning.

Depended on by mlr3verse.

Suggested by DALEXtra, flashlight, iml, mlr3viz, vip.


See at CRAN