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: 2.12.1
Depends: R (≥ 3.0.2), ParamHelpers (≥ 1.10)
Imports: BBmisc (≥ 1.11), backports (≥ 1.1.0), ggplot2, stats, stringi, checkmate (≥ 1.8.2), data.table, methods, parallelMap (≥ 1.3), survival, utils, XML
Suggests: ada, adabag, bartMachine, batchtools, brnn, bst, C50, care, caret (≥ 6.0-57), class, clue, cluster, clusterSim (≥ 0.44-5), clValid, cmaes, CoxBoost, crs, Cubist, deepnet, DiceKriging, DiceOptim, DiscriMiner, e1071, earth, elasticnet, elmNN, emoa, evtree, extraTrees, fda.usc, FDboost, flare, fields, FNN, fpc, frbs, FSelector, gbm, GenSA, glmnet, h2o (≥, GPfit, Hmisc, ipred, irace (≥ 2.0), kernlab, kknn, klaR, knitr, laGP, LiblineaR, lintr (≥, lqa, MASS, mboost, mco, mda, mlbench, mldr, mlrMBO, mmpf, modeltools, mRMRe, nnet, nodeHarvest (≥ 0.7-3), neuralnet, numDeriv, pamr, party, penalized (≥ 0.9-47), pls, PMCMR (≥ 4.1), randomForest, randomForestSRC (≥ 2.2.0), ranger (≥ 0.8.0), refund, rex, rFerns, rknn, rmarkdown, robustbase, ROCR, rotationForest, rpart, RRF, rrlda, rsm, RSNNS, RWeka, sda, shiny, smoof, sparseLDA, stepPlr, survAUC, SwarmSVM, svglite, testthat, tgp,, wavelets, xgboost (≥ 0.6-2)
Published: 2018-03-29
Author: Bernd Bischl [aut, cre], Michel Lang [aut], Lars Kotthoff [aut], 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], Patrick Schratz [ctb], Steve Bronder [ctb]
Maintainer: Bernd Bischl <bernd_bischl at>
License: BSD_2_clause + file LICENSE
NeedsCompilation: yes
Citation: mlr citation info
Materials: NEWS
In views: MachineLearning
CRAN checks: mlr results


Reference manual: mlr.pdf
Vignettes: mlr
Package source: mlr_2.12.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: mlr_2.12.1.tgz, r-oldrel: mlr_2.11.tgz
Old sources: mlr archive

Reverse dependencies:

Reverse depends: llama, mlrCPO, mlrMBO, OOBCurve, OpenML, RBPcurve, spFSR, unbalanced
Reverse imports: aslib, flacco, live, tuneRanger
Reverse suggests: bnclassify, featurefinder, iml, lime, plotmo
Reverse enhances: liquidSVM


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