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.

Version: 2.19.2
Depends: ParamHelpers (≥ 1.10), R (≥ 3.0.2)
Imports: backports (≥ 1.1.0), BBmisc (≥ 1.11), checkmate (≥ 1.8.2), data.table (≥ 1.12.4), ggplot2, methods, parallelMap (≥ 1.3), stats, stringi, survival, utils, XML
Suggests: ada, adabag, batchtools, bit64, brnn, bst, C50, care, caret (≥ 6.0-57), class, clue, cluster, ClusterR, clusterSim (≥ 0.44-5), cmaes, cowplot, crs, Cubist, deepnet, DiceKriging, e1071, earth, elasticnet, emoa, evtree, fda.usc, FDboost, FNN, forecast (≥ 8.3), fpc, frbs, FSelector, FSelectorRcpp (≥ 0.3.5), gbm, GenSA, ggpubr, glmnet, GPfit, h2o (≥, Hmisc, irace (≥ 2.0), kernlab, kknn, klaR, knitr, laGP, LiblineaR, lintr (≥, MASS, mboost, mco, mda, memoise, mlbench, mldr, mlrMBO, modeltools, mRMRe, neuralnet, nnet, numDeriv, pamr, pander, party, pec, penalized (≥ 0.9-47), pls, PMCMRplus, praznik (≥ 5.0.0), randomForest, ranger (≥ 0.8.0), rappdirs, refund, rex, rFerns, rgenoud, rmarkdown, Rmpi, ROCR, rotationForest, rpart, RRF, rsm, RSNNS, rucrdtw, RWeka, sda, sf, smoof, sparseLDA, stepPlr, survAUC, svglite, testthat, tgp,, tidyr, tsfeatures, vdiffr, wavelets, xgboost (≥ 0.7)
Published: 2024-06-12
DOI: 10.32614/CRAN.package.mlr
Author: Bernd Bischl ORCID iD [aut], Michel Lang ORCID iD [aut], Lars Kotthoff [aut], Patrick Schratz ORCID iD [aut], Julia Schiffner [aut], Jakob Richter [aut], Zachary Jones [aut], Giuseppe Casalicchio ORCID iD [aut], Mason Gallo [aut], Jakob Bossek ORCID iD [ctb], Erich Studerus ORCID iD [ctb], Leonard Judt [ctb], Tobias Kuehn [ctb], Pascal Kerschke ORCID iD [ctb], Florian Fendt [ctb], Philipp Probst ORCID iD [ctb], Xudong Sun ORCID iD [ctb], Janek Thomas ORCID iD [ctb], Bruno Vieira [ctb], Laura Beggel ORCID iD [ctb], Quay Au ORCID iD [ctb], Martin Binder [aut, cre], Florian Pfisterer [ctb], Stefan Coors [ctb], Steve Bronder [ctb], Alexander Engelhardt [ctb], Christoph Molnar [ctb], Annette Spooner [ctb]
Maintainer: Martin Binder <mlr.developer at>
License: BSD_2_clause + file LICENSE
NeedsCompilation: yes
SystemRequirements: gdal (optional), geos (optional), proj (optional), udunits (optional), gsl (optional), gmp (optional), glu (optional), jags (optional), mpfr (optional), openmpi (optional)
Citation: mlr citation info
Materials: NEWS
CRAN checks: mlr results


Reference manual: mlr.pdf
Vignettes: mlr


Package source: mlr_2.19.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): mlr_2.19.2.tgz, r-oldrel (arm64): mlr_2.19.2.tgz, r-release (x86_64): mlr_2.19.2.tgz, r-oldrel (x86_64): mlr_2.19.2.tgz
Old sources: mlr archive

Reverse dependencies:

Reverse depends: llama, mlrCPO, mlrMBO, OOBCurve, RBPcurve
Reverse imports: aslib, flacco, ipfr, latentFactoR, live, nsga3, seqimpute, tramnet, tuneRanger, varycoef
Reverse suggests: bnclassify, ChemoSpec2D, condvis2, counterfactuals, DALEXtra, ecr, featurefinder, iml, irace, lime, mlrintermbo, OpenML, plotmo, r2pmml, vivid
Reverse enhances: vip


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