mlr: Machine Learning in R

Interface to a large number of classification and regression techniques, including machine-readable parameter descriptions. There is also an experimental extension for survival analysis, clustering and general, example-specific cost-sensitive learning. Generic resampling, including cross-validation, bootstrapping and subsampling. Hyperparameter tuning with modern optimization techniques, for single- and multi-objective problems. Filter and wrapper methods for feature selection. Extension of basic learners with additional operations common in machine learning, also allowing for easy nested resampling. Most operations can be parallelized.

Package details

AuthorBernd Bischl [aut] (<>), Michel Lang [aut] (<>), Lars Kotthoff [aut], Patrick Schratz [aut, cre] (<>), Julia Schiffner [aut], Jakob Richter [aut], Zachary Jones [aut], Giuseppe Casalicchio [aut] (<>), Mason Gallo [aut], Jakob Bossek [ctb] (<>), Erich Studerus [ctb] (<>), Leonard Judt [ctb], Tobias Kuehn [ctb], Pascal Kerschke [ctb] (<>), Florian Fendt [ctb], Philipp Probst [ctb] (<>), Xudong Sun [ctb] (<>), Janek Thomas [ctb] (<>), Bruno Vieira [ctb], Laura Beggel [ctb] (<>), Quay Au [ctb] (<>), Martin Binder [ctb], Florian Pfisterer [ctb], Stefan Coors [ctb], Steve Bronder [ctb], Alexander Engelhardt [ctb], Christoph Molnar [ctb], Annette Spooner [ctb]
MaintainerPatrick Schratz <>
LicenseBSD_2_clause + file LICENSE
Package repositoryView on CRAN
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mlr documentation built on Sept. 29, 2022, 5:05 p.m.