![]() ![]() It’s been around for a long time, and there are numerous resources, answers, and solutions to all the possible questions. R-devel (arm64): caret_6.0-94.tgz, r-release (arm64): caret_6.0-94.tgz, r-oldrel (arm64): caret_6.0-94.tgz, r-devel (x86_64): caret_6.0-94.tgz, r-release (x86_64): caret_6.0-94.tgz, r-oldrel (x86_64): caret_6.0-94.tgzĪdabag, AntAngioCOOL, AutoStepwiseGLM, branchpointer, dbcsp, fscaret, GWAS.BAYES, hsdar, iForecast, JQL, manymodelr, maPredictDSC, MLSeq, MobileTrigger, MRReg, MSclassifR, natstrat, RandPro, SpatialMLĪdaSampling, aggTrees, aLFQ, ampir, animalcules, assignPOP, autoBagging, BLRShiny, BLRShiny2, bnviewer, caretEnsemble, caretForecast, CAST, CEEMDANML, chemmodlab, ChIC, ChIC. The caret package (short for C lassification A nd RE gression T raining) streamlines the process for creating predictive models and has been the top choice among R users. However, they are distributed via different packages, developed by different authors, and often use different. ![]() R-devel: caret_6.0-94.zip, r-release: caret_6.0-94.zip, r-oldrel: caret_6.0-94.zip Many of these algorithms are implemented in R. Misc functions for training and plotting classification andĮ1071, foreach, grDevices, methods, ModelMetrics (≥ 1.2.2.2), nlme, plyr, pROC, recipes (≥ 0.1.10), reshape2, stats, stats4, utils, withr (≥ 2.0.0)īradleyTerry2, covr, Cubist, dplyr, earth (≥ 2.2-3), ellipse, fastICA, gam (≥ 1.15), ipred, kernlab, klaR, knitr, MASS, Matrix, mda, mgcv, mlbench, MLmetrics, nnet, pamr, party (≥ 0.9-99992), pls, proxy, randomForest, RANN, rmarkdown, rpart, spls, subselect, superpc, testthat (≥ 0.9.1), themis (≥ 0.1.3)Ī Short Introduction to the caret Package Caret: Classification and Regression Training ![]()
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