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.


Reference manual

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0.13.0 by Michel Lang, 11 days ago,

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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] , Marc Becker [ctb]

Documentation:   PDF Manual  

Task views: Machine Learning & Statistical Learning

LGPL-3 license

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

Suggests Matrix, callr, codetools, datasets, distr6, evaluate, future.callr, mlr3data, progressr, remotes, rpart, testthat

Imported by DoubleML, autohd, highMLR, mcboost, mlr3filters, mlr3hyperband, mlr3oml, mlr3pipelines, mlr3shiny, mlr3spatiotempcv, mlr3tuningspaces, sense.

Depended on by GenericML, NADIA, mlr3cluster, mlr3db, mlr3fselect, mlr3learners, mlr3proba, mlr3spatial, mlr3tuning, mlr3verse.

Suggested by DALEXtra, condvis2, flashlight, iml, mlr3benchmark, mlr3data, mlr3viz, mlrintermbo, vip, vivid.

See at CRAN