mlr (2.4)

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.

Maintainer: Bernd Bischl
Author(s): Bernd Bischl [aut, cre], Michel Lang [aut], Jakob Richter [aut], Jakob Bossek [aut], Leonard Judt [aut], Tobias Kuehn [aut], Erich Studerus [aut], Lars Kotthoff [aut], Zachary Jones [ctb]

License: BSD_2_clause + file LICENSE

Uses: BBmisc, checkmate, ggplot2, ggvis, parallelMap, ParamHelpers, plyr, reshape2, shiny, survival, Hmisc, ROCR, ada, adabag, caret, clValid, clue, clusterSim, cluster, e1071, earth, elasticnet, gbm, kernlab, kknn, klaR, kohonen, mboost, mda, mlbench, modeltools, pamr, party, penalized, pls, randomForest, rjson, robustbase, rpart, stepPlr, tgp, RWeka, CoxBoost, RCurl, glmnet, mco, FSelector, rsm, sda, sparseLDA, nodeHarvest, MASS, class, nnet, testthat, DiceKriging, DiceOptim, FNN, LiblineaR, cmaes, emoa, lqa, pROC, care, GenSA, bst, Cubist, crs, rrlda, rFerns, knitr, irace, elmNN, mRMRe, DiscriMiner, frbs, randomForestSRC, flare, extraTrees, brnn, sparsediscrim,, laGP, bartMachine, rmarkdown
Reverse depends: flacco, llama, mlrCPO, mlrMBO, OOBCurve, OpenML, RBPcurve, spFSR, unbalanced
Reverse suggests: ChemoSpec2D, compboost, DALEXtra, featurefinder, flacco, iml, irace, lime, modelplotr, OpenML, plotmo
Reverse enhances: liquidSVM

Released over 4 years ago.