caret v5.10-13


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by Max Kuhn

Classification and Regression Training

Misc functions for training and plotting classification and regression models

Functions in caret

Name Description
confusionMatrix.train Estimate a Resampled Confusion Matrix
diff.resamples Inferential Assessments About Model Performance
nullModel Fit a simple, non-informative model
knnreg k-Nearest Neighbour Regression
BoxCoxTrans.default Box-Cox Transformations
nearZeroVar Identification of near zero variance predictors
BloodBrain Blood Brain Barrier Data
plotClassProbs Plot Predicted Probabilities in Classification Models
cars Kelly Blue Book resale data for 2005 model year GM cars
modelLookup Descriptions Of Models Available in train()
featurePlot Wrapper for Lattice Plotting of Predictor Variables
Alternate Affy Gene Expression Summary Methods. Generate Expression Values from Probes
dummyVars Create A Full Set of Dummy Variables
lattice.rfe Lattice functions for plotting resampling results of recursive feature selection
aucRoc Compute the area under an ROC curve
as.table.confusionMatrix Save Confusion Table Results
resampleSummary Summary of resampled performance estimates
print.confusionMatrix Print method for confusionMatrix
filterVarImp Calculation of filter-based variable importance
print.train Print Method for the train Class
dotplot.diff.resamples Lattice Functions for Visualizing Resampling Differences
predict.train Extract predictions and class probabilities from train objects
createDataPartition Data Splitting functions
rfe Backwards Feature Selection
findLinearCombos Determine linear combinations in a matrix
summary.bagEarth Summarize a bagged earth or FDA fit
xyplot.resamples Lattice Functions for Visualizing Resampling Results
histogram.train Lattice functions for plotting resampling results
confusionMatrix Create a confusion matrix
prcomp.resamples Principal Components Analysis of Resampling Results
caret-internal Internal Functions
findCorrelation Determine highly correlated variables
roc Compute the points for an ROC curve
icr.formula Independent Component Regression
resampleHist Plot the resampling distribution of the model statistics
knn3 k-Nearest Neighbour Classification
pottery Pottery from Pre-Classical Sites in Italy
panel.lift2 Lattice Panel Functions for Lift Plots
sbfControl Control Object for Selection By Filtering (SBF)
postResample Calculates performance across resamples
varImp Calculation of variable importance for regression and classification models
createGrid Tuning Parameter Grid
predictors List predictors used in the model
trainControl Control parameters for train
plsda Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
plot.varImp.train Plotting variable importance measures
predict.knn3 Predictions from k-Nearest Neighbors
dotPlot Create a dotplot of variable importance values
caretFuncs Backwards Feature Selection Helper Functions
GermanCredit German Credit Data
lift Lift Plot
mdrr Multidrug Resistance Reversal (MDRR) Agent Data
bagFDA Bagged FDA
dhfr Dihydrofolate Reductase Inhibitors Data
panel.needle Needle Plot Lattice Panel
bag.default A General Framework For Bagging
maxDissim Maximum Dissimilarity Sampling
predict.bagEarth Predicted values based on bagged Earth and FDA models
classDist Compute and predict the distances to class centroids
preProcess Pre-Processing of Predictors
resamples Collation and Visualization of Resampling Results
rfeControl Controlling the Feature Selection Algorithms
format.bagEarth Format 'bagEarth' objects
predict.knnreg Predictions from k-Nearest Neighbors Regression Model
bagEarth Bagged Earth
normalize.AffyBatch.normalize2Reference Quantile Normalization to a Reference Distribution
plotObsVsPred Plot Observed versus Predicted Results in Regression and Classification Models
sbf Selection By Filtering (SBF)
pcaNNet.default Neural Networks with a Principal Component Step
sensitivity Calculate sensitivity, specificity and predictive values
spatialSign Compute the multivariate spatial sign
segmentationData Cell Body Segmentation
train Fit Predictive Models over Different Tuning Parameters
oneSE Selecting tuning Parameters
cox2 COX-2 Activity Data
oil Fatty acid composition of commercial oils
normalize2Reference Quantile Normalize Columns of a Matrix Based on a Reference Distribution
avNNet.default Neural Networks Using Model Averaging
tecator Fat, Water and Protein Content of Meat Samples
plot.train Plot Method for the train Class
caretSBF Selection By Filtering (SBF) Helper Functions
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