Package: caret 6.0-94

caret: Classification and Regression Training

Misc functions for training and plotting classification and regression models.

Authors:Max Kuhn [aut, cre], Jed Wing [ctb], Steve Weston [ctb], Andre Williams [ctb], Chris Keefer [ctb], Allan Engelhardt [ctb], Tony Cooper [ctb], Zachary Mayer [ctb], Brenton Kenkel [ctb], R Core Team [ctb], Michael Benesty [ctb], Reynald Lescarbeau [ctb], Andrew Ziem [ctb], Luca Scrucca [ctb], Yuan Tang [ctb], Can Candan [ctb], Tyler Hunt [ctb]

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NEWS

# Install 'caret' in R:

install.packages('caret', repos = c('https://topepo.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/topepo/caret/issues

Datasets:

GermanCredit - German Credit Data
Sacramento - Sacramento CA Home Prices
absorp - Fat, Water and Protein Content of Meat Samples
bbbDescr - Blood Brain Barrier Data
cars - Kelly Blue Book resale data for 2005 model year GM cars
cox2Class - COX-2 Activity Data
cox2Descr - COX-2 Activity Data
cox2IC50 - COX-2 Activity Data
dhfr - Dihydrofolate Reductase Inhibitors Data
endpoints - Fat, Water and Protein Content of Meat Samples
fattyAcids - Fatty acid composition of commercial oils
logBBB - Blood Brain Barrier Data
mdrrClass - Multidrug Resistance Reversal (MDRR) Agent Data
mdrrDescr - Multidrug Resistance Reversal (MDRR) Agent Data
oilType - Fatty acid composition of commercial oils
potteryClass - Pottery from Pre-Classical Sites in Italy
scat - Morphometric Data on Scat
scat_orig - Morphometric Data on Scat
segmentationData - Cell Body Segmentation

On CRAN:

18.87 score 1.6k stars 287 packages 56k scripts 202k downloads 630 mentions 179 exports 74 dependencies

Last updated 2 years agofrom:5f4bd2069b. Checks:OK: 1 NOTE: 8. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Nov 06 2024
R-4.5-win-x86_64	NOTE	Nov 06 2024
R-4.5-linux-x86_64	NOTE	Nov 06 2024
R-4.4-win-x86_64	NOTE	Nov 06 2024
R-4.4-mac-x86_64	NOTE	Nov 06 2024
R-4.4-mac-aarch64	NOTE	Nov 06 2024
R-4.3-win-x86_64	NOTE	Nov 06 2024
R-4.3-mac-x86_64	NOTE	Nov 06 2024
R-4.3-mac-aarch64	NOTE	Nov 06 2024

Exports:anovaScores avNNet bag bagControl bagEarth bagEarthStats bagFDA best BoxCoxTrans calibration caretFuncs caretGA caretSA caretSBF caretTheme cforestStats checkConditionalX checkInstall checkResamples class2ind classDist cluster compare_models confusionMatrix confusionMatrix.train contr.dummy contr.ltfr createDataPartition createFolds createModel createMultiFolds createResample createTimeSlices ctreeBag defaultSummary dotPlot downSample dummyVars expandParameters expoTrans extractPrediction extractProb F_meas featurePlot filterVarImp findCorrelation findLinearCombos flatTable gafs gafs_initial gafs_lrSelection gafs_raMutation gafs_rwSelection gafs_spCrossover gafs_tourSelection gafs_uCrossover gafs.default gafsControl gamFormula gamFuncs gamScores getModelInfo getSamplingInfo getTrainPerf ggplot.gafs ggplot.safs groupKFold hasTerms icr index2vec ipredStats knn3 knn3Train knnreg knnregTrain ldaBag ldaFuncs ldaSBF learning_curve_dat lift lmFuncs lmSBF LPH07_1 LPH07_2 lrFuncs MAE maxDissim MeanSD minDiss mnLogLoss modelCor modelLookup multiClassSummary nbBag nbFuncs nbSBF nearZeroVar negPredValue nnetBag nullModel nzv oneSE outcome_conversion panel.calibration panel.lift panel.lift2 panel.needle pcaNNet pickSizeBest pickSizeTolerance pickVars plot.gafs plot.rfe plot.safs plot.train plotClassProbs plotObsVsPred plsBag plsda posPredValue postResample precision predict.bagEarth predict.gafs predict.train predictionFunction predictors preProcess print.train probFunction progress prSummary R2 recall resampleHist resamples resampleSummary resampleWrapper rfe rfeControl rfeIter rfFuncs rfGA rfSA rfSBF rfStats RMSE safs safs_initial safs_perturb safs_prob safsControl sbf sbfControl sbfIter sensitivity SLC14_1 SLC14_2 sortImp spatialSign specificity splsda sumDiss summary.bagEarth svmBag thresholder tolerance train trainControl treebagFuncs treebagGA treebagSA treebagSBF twoClassSim twoClassSummary upSample var_seq varImp well_numbered

Dependencies:class cli clock codetools colorspace cpp11 data.table diagram digest dplyr e1071 fansi farver foreach future future.apply generics ggplot2 globals glue gower gtable hardhat ipred isoband iterators KernSmooth labeling lattice lava lifecycle listenv lubridate magrittr MASS Matrix mgcv ModelMetrics munsell nlme nnet numDeriv parallelly pillar pkgconfig plyr pROC prodlim progressr proxy purrr R6 RColorBrewer Rcpp recipes reshape2 rlang rpart scales shape SQUAREM stringi stringr survival tibble tidyr tidyselect timechange timeDate tzdb utf8 vctrs viridisLite withr

A Short Introduction to the caret Package

Rendered fromcaret.Rmdusingknitr::rmarkdownon Nov 06 2024.

Last update: 2021-10-08
Started: 2018-03-02

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Confusion matrix as a table	as.matrix.confusionMatrix as.table.confusionMatrix
Neural Networks Using Model Averaging	avNNet avNNet.default avNNet.formula predict.avNNet print.avNNet
A General Framework For Bagging	bag bag.default bagControl ctreeBag ldaBag nbBag nnetBag plsBag predict.bag print.bag print.summary.bag summary.bag svmBag
Bagged Earth	bagEarth bagEarth.default bagEarth.formula print.bagEarth
Bagged FDA	bagFDA bagFDA.default bagFDA.formula print.bagFDA
Blood Brain Barrier Data	bbbDescr BloodBrain logBBB
Box-Cox and Exponential Transformations	BoxCoxTrans BoxCoxTrans.default expoTrans expoTrans.default predict.BoxCoxTrans predict.expoTrans print.BoxCoxTrans
Probability Calibration Plot	calibration calibration.default calibration.formula ggplot.calibration panel.calibration print.calibration xyplot.calibration
Selection By Filtering (SBF) Helper Functions	anovaScores caretSBF gamScores ldaSBF lmSBF nbSBF rfSBF treebagSBF
Kelly Blue Book resale data for 2005 model year GM cars	cars
Compute and predict the distances to class centroids	classDist classDist.default predict.classDist
Create a confusion matrix	confusionMatrix confusionMatrix.default confusionMatrix.matrix confusionMatrix.table
Estimate a Resampled Confusion Matrix	confusionMatrix.rfe confusionMatrix.sbf confusionMatrix.train
COX-2 Activity Data	cox2 cox2Class cox2Descr cox2IC50
Data Splitting functions	createDataPartition createFolds createMultiFolds createResample createTimeSlices groupKFold
Calculates performance across resamples	defaultSummary getTrainPerf MAE mnLogLoss multiClassSummary postResample prSummary R2 RMSE twoClassSummary
Lattice functions for plotting resampling results of recursive feature selection	densityplot.rfe histogram.rfe stripplot.rfe xyplot.rfe
Dihydrofolate Reductase Inhibitors Data	dhfr
Inferential Assessments About Model Performance	compare_models diff.resamples summary.diff.resamples
Create a dotplot of variable importance values	dotPlot
Lattice Functions for Visualizing Resampling Differences	bwplot.diff.resamples densityplot.diff.resamples dotplot.diff.resamples levelplot.diff.resamples
Down- and Up-Sampling Imbalanced Data	downSample upSample
Create A Full Set of Dummy Variables	class2ind contr.dummy contr.ltfr dummyVars dummyVars.default predict.dummyVars print.dummyVars
Extract predictions and class probabilities from train objects	extractPrediction extractProb predict.list predict.train
Wrapper for Lattice Plotting of Predictor Variables	featurePlot
Calculation of filter-based variable importance	filterVarImp
Determine highly correlated variables	findCorrelation
Determine linear combinations in a matrix	findLinearCombos
Format 'bagEarth' objects	format.bagEarth
Ancillary genetic algorithm functions	caretGA gafs_initial gafs_lrSelection gafs_nlrSelection gafs_raMutation gafs_rwSelection gafs_spCrossover gafs_tourSelection gafs_uCrossover rfGA treebagGA
Genetic algorithm feature selection	gafs gafs.default gafs.recipe
Control parameters for GA and SA feature selection	gafsControl safsControl
German Credit Data	GermanCredit
Get sampling info from a train model	getSamplingInfo
Plot RFE Performance Profiles	ggplot.rfe plot.rfe
Plot Method for the train Class	ggplot.train plot.train
Lattice functions for plotting resampling results	densityplot.train histogram.train stripplot.train xyplot.train
Independent Component Regression	icr icr.default icr.formula predict.icr
Convert indicies to a binary vector	index2vec
k-Nearest Neighbour Classification	knn3 knn3.data.frame knn3.formula knn3.matrix knn3Train print.knn3
k-Nearest Neighbour Regression	knnreg knnreg.data.frame knnreg.default knnreg.formula knnreg.matrix knnregTrain print.knnreg
Create Data to Plot a Learning Curve	learning_curve_dat
Lift Plot	ggplot.lift lift lift.default lift.formula print.lift xyplot.lift
Maximum Dissimilarity Sampling	maxDissim minDiss sumDiss
Multidrug Resistance Reversal (MDRR) Agent Data	mdrr mdrrClass mdrrDescr
Tools for Models Available in 'train'	checkInstall getModelInfo modelLookup
Identification of near zero variance predictors	checkConditionalX checkResamples nearZeroVar nzv
Calculate sensitivity, specificity and predictive values	negPredValue negPredValue.default negPredValue.matrix negPredValue.table posPredValue posPredValue.default posPredValue.matrix posPredValue.table sensitivity sensitivity.default sensitivity.matrix sensitivity.table specificity specificity.default specificity.matrix specificity.table
Fit a simple, non-informative model	nullModel nullModel.default predict.nullModel
Fatty acid composition of commercial oils	fattyAcids oil oilType
Selecting tuning Parameters	best oneSE tolerance
Lattice Panel Functions for Lift Plots	panel.lift panel.lift2
Needle Plot Lattice Panel	panel.needle
Neural Networks with a Principal Component Step	pcaNNet pcaNNet.default pcaNNet.formula predict.pcaNNet print.pcaNNet
Backwards Feature Selection Helper Functions	caretFuncs gamFuncs ldaFuncs lmFuncs lrFuncs nbFuncs pickSizeBest pickSizeTolerance pickVars rfFuncs treebagFuncs
Plot Method for the gafs and safs Classes	ggplot.gafs ggplot.safs plot.gafs plot.safs
Plotting variable importance measures	ggplot.varImp.train plot.varImp.train
Plot Predicted Probabilities in Classification Models	plotClassProbs
Plot Observed versus Predicted Results in Regression and Classification Models	plotObsVsPred
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis	plsda plsda.default predict.plsda predict.splsda splsda splsda.default
Pottery from Pre-Classical Sites in Italy	pottery potteryClass
Principal Components Analysis of Resampling Results	cluster cluster.resamples plot.prcomp.resamples prcomp.resamples
Predicted values based on bagged Earth and FDA models	predict.bagEarth predict.bagFDA
Predict new samples	predict.gafs predict.safs
Predictions from k-Nearest Neighbors	predict.knn3
Predictions from k-Nearest Neighbors Regression Model	predict.knnreg
List predictors used in the model	predictors predictors.default predictors.formula predictors.list predictors.rfe predictors.sbf predictors.terms predictors.train
Pre-Processing of Predictors	predict.preProcess preProcess preProcess.default
Print method for confusionMatrix	print.confusionMatrix
Print Method for the train Class	print.train
Calculate recall, precision and F values	F_meas F_meas.default F_meas.table precision precision.default precision.matrix precision.table recall recall.default recall.table
Plot the resampling distribution of the model statistics	resampleHist
Collation and Visualization of Resampling Results	as.data.frame.resamples as.matrix.resamples modelCor print.resamples resamples resamples.default sort.resamples summary.resamples
Summary of resampled performance estimates	resampleSummary
Backwards Feature Selection	predict.rfe rfe rfe.default rfe.formula rfe.recipe rfeIter update.rfe
Controlling the Feature Selection Algorithms	rfeControl
Sacramento CA Home Prices	Sacramento
Simulated annealing feature selection	safs safs.default safs.recipe
Ancillary simulated annealing functions	caretSA rfSA safs_initial safs_perturb safs_prob treebagSA
Selection By Filtering (SBF)	predict.sbf sbf sbf.default sbf.formula sbf.recipe
Control Object for Selection By Filtering (SBF)	sbfControl
Morphometric Data on Scat	scat scat_orig
Cell Body Segmentation	segmentationData
Simulation Functions	LPH07_1 LPH07_2 SLC14_1 SLC14_2 twoClassSim
Compute the multivariate spatial sign	spatialSign spatialSign.data.frame spatialSign.default spatialSign.matrix
Summarize a bagged earth or FDA fit	summary.bagEarth summary.bagFDA
Fat, Water and Protein Content of Meat Samples	absorp endpoints tecator
Generate Data to Choose a Probability Threshold	thresholder
Fit Predictive Models over Different Tuning Parameters	train train.default train.formula train.recipe
A List of Available Models in train	models train_model_list
Control parameters for train	trainControl
Update or Re-fit a SA or GA Model	update.gafs update.safs
Update or Re-fit a Model	update.train
Sequences of Variables for Tuning	var_seq
Calculation of variable importance for regression and classification models	varImp varImp.avNNet varImp.bagEarth varImp.bagFDA varImp.C5.0 varImp.classbagg varImp.cubist varImp.dsa varImp.earth varImp.fda varImp.Gam varImp.gam varImp.gbm varImp.glm varImp.glmnet varImp.JRip varImp.lm varImp.multinom varImp.mvr varImp.nnet varImp.pamrtrained varImp.PART varImp.plsda varImp.RandomForest varImp.randomForest varImp.regbagg varImp.rfe varImp.rpart varImp.RRF varImp.train
Variable importances for GAs and SAs	varImp.gafs varImp.safs
Lattice Functions for Visualizing Resampling Results	bwplot.resamples densityplot.resamples dotplot.resamples ggplot.resamples parallelplot.resamples splom.resamples xyplot.resamples