Classification and Regression Training


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Documentation for package ‘caret’ version 6.0-86

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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.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