Classification and Regression Training

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Documentation for package ‘caret’ version 6.0-94

• 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