R: Classification and Regression Training

# Classification and Regression Training

## Documentation for package ‘caret’ version 7.0-1

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS

## Help Pages

A B C D E F G H I K L M N O P R S T U V X

## -- A --

absorp | Fat, Water and Protein Content of Meat Samples
anovaScores | Selection By Filtering (SBF) Helper Functions
as.data.frame.resamples | Collation and Visualization of Resampling Results
as.matrix.confusionMatrix | Confusion matrix as a table
as.matrix.resamples | Collation and Visualization of Resampling Results
as.table.confusionMatrix | Confusion matrix as a table
avNNet | Neural Networks Using Model Averaging
avNNet.default | Neural Networks Using Model Averaging
avNNet.formula | Neural Networks Using Model Averaging

## -- B --

bag | A General Framework For Bagging
bag.default | A General Framework For Bagging
bagControl | A General Framework For Bagging
bagEarth | Bagged Earth
bagEarth.default | Bagged Earth
bagEarth.formula | Bagged Earth
bagFDA | Bagged FDA
bagFDA.default | Bagged FDA
bagFDA.formula | Bagged FDA
bbbDescr | Blood Brain Barrier Data
best | Selecting tuning Parameters
BloodBrain | Blood Brain Barrier Data
BoxCoxTrans | Box-Cox and Exponential Transformations
BoxCoxTrans.default | Box-Cox and Exponential Transformations
bwplot.diff.resamples | Lattice Functions for Visualizing Resampling Differences
bwplot.resamples | Lattice Functions for Visualizing Resampling Results

## -- C --

calibration | Probability Calibration Plot
calibration.default | Probability Calibration Plot
calibration.formula | Probability Calibration Plot
caretFuncs | Backwards Feature Selection Helper Functions
caretGA | Ancillary genetic algorithm functions
caretSA | Ancillary simulated annealing functions
caretSBF | Selection By Filtering (SBF) Helper Functions
cars | Kelly Blue Book resale data for 2005 model year GM cars
checkConditionalX | Identification of near zero variance predictors
checkInstall | Tools for Models Available in 'train'
checkResamples | Identification of near zero variance predictors
class2ind | Create A Full Set of Dummy Variables
classDist | Compute and predict the distances to class centroids
classDist.default | Compute and predict the distances to class centroids
cluster | Principal Components Analysis of Resampling Results
cluster.resamples | Principal Components Analysis of Resampling Results
compare_models | Inferential Assessments About Model Performance
confusionMatrix | Create a confusion matrix
confusionMatrix.default | Create a confusion matrix
confusionMatrix.matrix | Create a confusion matrix
confusionMatrix.rfe | Estimate a Resampled Confusion Matrix
confusionMatrix.sbf | Estimate a Resampled Confusion Matrix
confusionMatrix.table | Create a confusion matrix
confusionMatrix.train | Estimate a Resampled Confusion Matrix
contr.dummy | Create A Full Set of Dummy Variables
contr.ltfr | Create A Full Set of Dummy Variables
cox2 | COX-2 Activity Data
cox2Class | COX-2 Activity Data
cox2Descr | COX-2 Activity Data
cox2IC50 | COX-2 Activity Data
createDataPartition | Data Splitting functions
createFolds | Data Splitting functions
createMultiFolds | Data Splitting functions
createResample | Data Splitting functions
createTimeSlices | Data Splitting functions
ctreeBag | A General Framework For Bagging

## -- D --

defaultSummary | Calculates performance across resamples
densityplot.diff.resamples | Lattice Functions for Visualizing Resampling Differences
densityplot.resamples | Lattice Functions for Visualizing Resampling Results
densityplot.rfe | Lattice functions for plotting resampling results of recursive feature selection
densityplot.train | Lattice functions for plotting resampling results
dhfr | Dihydrofolate Reductase Inhibitors Data
diff.resamples | Inferential Assessments About Model Performance
dotPlot | Create a dotplot of variable importance values
dotplot.diff.resamples | Lattice Functions for Visualizing Resampling Differences
dotplot.resamples | Lattice Functions for Visualizing Resampling Results
downSample | Down- and Up-Sampling Imbalanced Data
dummyVars | Create A Full Set of Dummy Variables
dummyVars.default | Create A Full Set of Dummy Variables

## -- E --

endpoints | Fat, Water and Protein Content of Meat Samples
expoTrans | Box-Cox and Exponential Transformations
expoTrans.default | Box-Cox and Exponential Transformations
extractPrediction | Extract predictions and class probabilities from train objects
extractProb | Extract predictions and class probabilities from train objects

## -- F --

fattyAcids | Fatty acid composition of commercial oils
featurePlot | Wrapper for Lattice Plotting of Predictor Variables
filterVarImp | Calculation of filter-based variable importance
findCorrelation | Determine highly correlated variables
findLinearCombos | Determine linear combinations in a matrix
format.bagEarth | Format 'bagEarth' objects
F_meas | Calculate recall, precision and F values
F_meas.default | Calculate recall, precision and F values
F_meas.table | Calculate recall, precision and F values

## -- G --

gafs | Genetic algorithm feature selection
gafs.default | Genetic algorithm feature selection
gafs.recipe | Genetic algorithm feature selection
gafsControl | Control parameters for GA and SA feature selection
gafs_initial | Ancillary genetic algorithm functions
gafs_lrSelection | Ancillary genetic algorithm functions
gafs_nlrSelection | Ancillary genetic algorithm functions
gafs_raMutation | Ancillary genetic algorithm functions
gafs_rwSelection | Ancillary genetic algorithm functions
gafs_spCrossover | Ancillary genetic algorithm functions
gafs_tourSelection | Ancillary genetic algorithm functions
gafs_uCrossover | Ancillary genetic algorithm functions
gamFuncs | Backwards Feature Selection Helper Functions
gamScores | Selection By Filtering (SBF) Helper Functions
GermanCredit | German Credit Data
getModelInfo | Tools for Models Available in 'train'
getSamplingInfo | Get sampling info from a train model
getTrainPerf | Calculates performance across resamples
ggplot.calibration | Probability Calibration Plot
ggplot.gafs | Plot Method for the gafs and safs Classes
ggplot.lift | Lift Plot
ggplot.resamples | Lattice Functions for Visualizing Resampling Results
ggplot.rfe | Plot RFE Performance Profiles
ggplot.safs | Plot Method for the gafs and safs Classes
ggplot.train | Plot Method for the train Class
ggplot.varImp.train | Plotting variable importance measures
groupKFold | Data Splitting functions

## -- H --

histogram.rfe | Lattice functions for plotting resampling results of recursive feature selection
histogram.train | Lattice functions for plotting resampling results

## -- I --

icr | Independent Component Regression
icr.default | Independent Component Regression
icr.formula | Independent Component Regression
index2vec | Convert indicies to a binary vector

## -- K --

knn3 | k-Nearest Neighbour Classification
knn3.data.frame | k-Nearest Neighbour Classification
knn3.formula | k-Nearest Neighbour Classification
knn3.matrix | k-Nearest Neighbour Classification
knn3Train | k-Nearest Neighbour Classification
knnreg | k-Nearest Neighbour Regression
knnreg.data.frame | k-Nearest Neighbour Regression
knnreg.default | k-Nearest Neighbour Regression
knnreg.formula | k-Nearest Neighbour Regression
knnreg.matrix | k-Nearest Neighbour Regression
knnregTrain | k-Nearest Neighbour Regression

## -- L --

ldaBag | A General Framework For Bagging
ldaFuncs | Backwards Feature Selection Helper Functions
ldaSBF | Selection By Filtering (SBF) Helper Functions
learning_curve_dat | Create Data to Plot a Learning Curve
levelplot.diff.resamples | Lattice Functions for Visualizing Resampling Differences
lift | Lift Plot
lift.default | Lift Plot
lift.formula | Lift Plot
lmFuncs | Backwards Feature Selection Helper Functions
lmSBF | Selection By Filtering (SBF) Helper Functions
logBBB | Blood Brain Barrier Data
LPH07_1 | Simulation Functions
LPH07_2 | Simulation Functions
lrFuncs | Backwards Feature Selection Helper Functions

## -- M --

MAE | Calculates performance across resamples
maxDissim | Maximum Dissimilarity Sampling
mdrr | Multidrug Resistance Reversal (MDRR) Agent Data
mdrrClass | Multidrug Resistance Reversal (MDRR) Agent Data
mdrrDescr | Multidrug Resistance Reversal (MDRR) Agent Data
minDiss | Maximum Dissimilarity Sampling
mnLogLoss | Calculates performance across resamples
modelCor | Collation and Visualization of Resampling Results
modelLookup | Tools for Models Available in 'train'
models | A List of Available Models in train
multiClassSummary | Calculates performance across resamples

## -- N --

nbBag | A General Framework For Bagging
nbFuncs | Backwards Feature Selection Helper Functions
nbSBF | Selection By Filtering (SBF) Helper Functions
nearZeroVar | Identification of near zero variance predictors
negPredValue | Calculate sensitivity, specificity and predictive values
negPredValue.default | Calculate sensitivity, specificity and predictive values
negPredValue.matrix | Calculate sensitivity, specificity and predictive values
negPredValue.table | Calculate sensitivity, specificity and predictive values
nnetBag | A General Framework For Bagging
nullModel | Fit a simple, non-informative model
nullModel.default | Fit a simple, non-informative model
nzv | Identification of near zero variance predictors

## -- O --

oil | Fatty acid composition of commercial oils
oilType | Fatty acid composition of commercial oils
oneSE | Selecting tuning Parameters

## -- P --

panel.calibration | Probability Calibration Plot
panel.lift | Lattice Panel Functions for Lift Plots
panel.lift2 | Lattice Panel Functions for Lift Plots
panel.needle | Needle Plot Lattice Panel
parallelplot.resamples | Lattice Functions for Visualizing Resampling Results
pcaNNet | Neural Networks with a Principal Component Step
pcaNNet.default | Neural Networks with a Principal Component Step
pcaNNet.formula | Neural Networks with a Principal Component Step
pickSizeBest | Backwards Feature Selection Helper Functions
pickSizeTolerance | Backwards Feature Selection Helper Functions
pickVars | Backwards Feature Selection Helper Functions
plot.gafs | Plot Method for the gafs and safs Classes
plot.prcomp.resamples | Principal Components Analysis of Resampling Results
plot.rfe | Plot RFE Performance Profiles
plot.safs | Plot Method for the gafs and safs Classes
plot.train | Plot Method for the train Class
plot.varImp.train | Plotting variable importance measures
plotClassProbs | Plot Predicted Probabilities in Classification Models
plotObsVsPred | Plot Observed versus Predicted Results in Regression and Classification Models
plsBag | A General Framework For Bagging
plsda | Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
plsda.default | Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
posPredValue | Calculate sensitivity, specificity and predictive values
posPredValue.default | Calculate sensitivity, specificity and predictive values
posPredValue.matrix | Calculate sensitivity, specificity and predictive values
posPredValue.table | Calculate sensitivity, specificity and predictive values
postResample | Calculates performance across resamples
pottery | Pottery from Pre-Classical Sites in Italy
potteryClass | Pottery from Pre-Classical Sites in Italy
prcomp.resamples | Principal Components Analysis of Resampling Results
precision | Calculate recall, precision and F values
precision.default | Calculate recall, precision and F values
precision.matrix | Calculate recall, precision and F values
precision.table | Calculate recall, precision and F values
predict.avNNet | Neural Networks Using Model Averaging
predict.bag | A General Framework For Bagging
predict.bagEarth | Predicted values based on bagged Earth and FDA models
predict.bagFDA | Predicted values based on bagged Earth and FDA models
predict.BoxCoxTrans | Box-Cox and Exponential Transformations
predict.classDist | Compute and predict the distances to class centroids
predict.dummyVars | Create A Full Set of Dummy Variables
predict.expoTrans | Box-Cox and Exponential Transformations
predict.gafs | Predict new samples
predict.icr | Independent Component Regression
predict.knn3 | Predictions from k-Nearest Neighbors
predict.knnreg | Predictions from k-Nearest Neighbors Regression Model
predict.list | Extract predictions and class probabilities from train objects
predict.nullModel | Fit a simple, non-informative model
predict.pcaNNet | Neural Networks with a Principal Component Step
predict.plsda | Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
predict.preProcess | Pre-Processing of Predictors
predict.rfe | Backwards Feature Selection
predict.safs | Predict new samples
predict.sbf | Selection By Filtering (SBF)
predict.splsda | Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
predict.train | Extract predictions and class probabilities from train objects
predictors | List predictors used in the model
predictors.default | List predictors used in the model
predictors.formula | List predictors used in the model
predictors.list | List predictors used in the model
predictors.rfe | List predictors used in the model
predictors.sbf | List predictors used in the model
predictors.terms | List predictors used in the model
predictors.train | List predictors used in the model
preProcess | Pre-Processing of Predictors
preProcess.default | Pre-Processing of Predictors
print.avNNet | Neural Networks Using Model Averaging
print.bag | A General Framework For Bagging
print.bagEarth | Bagged Earth
print.bagFDA | Bagged FDA
print.BoxCoxTrans | Box-Cox and Exponential Transformations
print.calibration | Probability Calibration Plot
print.confusionMatrix | Print method for confusionMatrix
print.dummyVars | Create A Full Set of Dummy Variables
print.knn3 | k-Nearest Neighbour Classification
print.knnreg | k-Nearest Neighbour Regression
print.lift | Lift Plot
print.pcaNNet | Neural Networks with a Principal Component Step
print.resamples | Collation and Visualization of Resampling Results
print.summary.bag | A General Framework For Bagging
print.train | Print Method for the train Class
prSummary | Calculates performance across resamples

## -- R --

R2 | Calculates performance across resamples
recall | Calculate recall, precision and F values
recall.default | Calculate recall, precision and F values
recall.table | Calculate recall, precision and F values
resampleHist | Plot the resampling distribution of the model statistics
resamples | Collation and Visualization of Resampling Results
resamples.default | Collation and Visualization of Resampling Results
resampleSummary | Summary of resampled performance estimates
rfe | Backwards Feature Selection
rfe.default | Backwards Feature Selection
rfe.formula | Backwards Feature Selection
rfe.recipe | Backwards Feature Selection
rfeControl | Controlling the Feature Selection Algorithms
rfeIter | Backwards Feature Selection
rfFuncs | Backwards Feature Selection Helper Functions
rfGA | Ancillary genetic algorithm functions
rfSA | Ancillary simulated annealing functions
rfSBF | Selection By Filtering (SBF) Helper Functions
RMSE | Calculates performance across resamples

## -- S --

Sacramento | Sacramento CA Home Prices
safs | Simulated annealing feature selection
safs.default | Simulated annealing feature selection
safs.recipe | Simulated annealing feature selection
safsControl | Control parameters for GA and SA feature selection
safs_initial | Ancillary simulated annealing functions
safs_perturb | Ancillary simulated annealing functions
safs_prob | Ancillary simulated annealing functions
sbf | Selection By Filtering (SBF)
sbf.default | Selection By Filtering (SBF)
sbf.formula | Selection By Filtering (SBF)
sbf.recipe | Selection By Filtering (SBF)
sbfControl | Control Object for Selection By Filtering (SBF)
scat | Morphometric Data on Scat
scat_orig | Morphometric Data on Scat
segmentationData | Cell Body Segmentation
sensitivity | Calculate sensitivity, specificity and predictive values
sensitivity.default | Calculate sensitivity, specificity and predictive values
sensitivity.matrix | Calculate sensitivity, specificity and predictive values
sensitivity.table | Calculate sensitivity, specificity and predictive values
SLC14_1 | Simulation Functions
SLC14_2 | Simulation Functions
sort.resamples | Collation and Visualization of Resampling Results
spatialSign | Compute the multivariate spatial sign
spatialSign.data.frame | Compute the multivariate spatial sign
spatialSign.default | Compute the multivariate spatial sign
spatialSign.matrix | Compute the multivariate spatial sign
specificity | Calculate sensitivity, specificity and predictive values
specificity.default | Calculate sensitivity, specificity and predictive values
specificity.matrix | Calculate sensitivity, specificity and predictive values
specificity.table | Calculate sensitivity, specificity and predictive values
splom.resamples | Lattice Functions for Visualizing Resampling Results
splsda | Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
splsda.default | Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
stripplot.rfe | Lattice functions for plotting resampling results of recursive feature selection
stripplot.train | Lattice functions for plotting resampling results
sumDiss | Maximum Dissimilarity Sampling
summary.bag | A General Framework For Bagging
summary.bagEarth | Summarize a bagged earth or FDA fit
summary.bagFDA | Summarize a bagged earth or FDA fit
summary.diff.resamples | Inferential Assessments About Model Performance
summary.resamples | Collation and Visualization of Resampling Results
svmBag | A General Framework For Bagging

## -- T --

tecator | Fat, Water and Protein Content of Meat Samples
thresholder | Generate Data to Choose a Probability Threshold
tolerance | Selecting tuning Parameters
train | Fit Predictive Models over Different Tuning Parameters
train.default | Fit Predictive Models over Different Tuning Parameters
train.formula | Fit Predictive Models over Different Tuning Parameters
train.recipe | Fit Predictive Models over Different Tuning Parameters
trainControl | Control parameters for train
train_model_list | A List of Available Models in train
treebagFuncs | Backwards Feature Selection Helper Functions
treebagGA | Ancillary genetic algorithm functions
treebagSA | Ancillary simulated annealing functions
treebagSBF | Selection By Filtering (SBF) Helper Functions
twoClassSim | Simulation Functions
twoClassSummary | Calculates performance across resamples

## -- U --

update.gafs | Update or Re-fit a SA or GA Model
update.rfe | Backwards Feature Selection
update.safs | Update or Re-fit a SA or GA Model
update.train | Update or Re-fit a Model
upSample | Down- and Up-Sampling Imbalanced Data

## -- V --

varImp | Calculation of variable importance for regression and classification models
varImp.avNNet | Calculation of variable importance for regression and classification models
varImp.bagEarth | Calculation of variable importance for regression and classification models
varImp.bagFDA | Calculation of variable importance for regression and classification models
varImp.C5.0 | Calculation of variable importance for regression and classification models
varImp.classbagg | Calculation of variable importance for regression and classification models
varImp.cubist | Calculation of variable importance for regression and classification models
varImp.dsa | Calculation of variable importance for regression and classification models
varImp.earth | Calculation of variable importance for regression and classification models
varImp.fda | Calculation of variable importance for regression and classification models
varImp.gafs | Variable importances for GAs and SAs
varImp.Gam | Calculation of variable importance for regression and classification models
varImp.gam | Calculation of variable importance for regression and classification models
varImp.gbm | Calculation of variable importance for regression and classification models
varImp.glm | Calculation of variable importance for regression and classification models
varImp.glmnet | Calculation of variable importance for regression and classification models
varImp.JRip | Calculation of variable importance for regression and classification models
varImp.lm | Calculation of variable importance for regression and classification models
varImp.multinom | Calculation of variable importance for regression and classification models
varImp.mvr | Calculation of variable importance for regression and classification models
varImp.nnet | Calculation of variable importance for regression and classification models
varImp.pamrtrained | Calculation of variable importance for regression and classification models
varImp.PART | Calculation of variable importance for regression and classification models
varImp.plsda | Calculation of variable importance for regression and classification models
varImp.RandomForest | Calculation of variable importance for regression and classification models
varImp.randomForest | Calculation of variable importance for regression and classification models
varImp.regbagg | Calculation of variable importance for regression and classification models
varImp.rfe | Calculation of variable importance for regression and classification models
varImp.rpart | Calculation of variable importance for regression and classification models
varImp.RRF | Calculation of variable importance for regression and classification models
varImp.safs | Variable importances for GAs and SAs
varImp.train | Calculation of variable importance for regression and classification models
var_seq | Sequences of Variables for Tuning

## -- X --

xyplot.calibration | Probability Calibration Plot
xyplot.lift | Lift Plot
xyplot.resamples | Lattice Functions for Visualizing Resampling Results
xyplot.rfe | Lattice functions for plotting resampling results of recursive feature selection
xyplot.train | Lattice functions for plotting resampling results