caret v4.11


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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
rfeControl Controlling the Feature Selection Algorithms
plotClassProbs Plot Predicted Probabilities in Classification Models
aucRoc Compute the area under an ROC curve
createGrid Tuning Parameter Grid
bagFDA Bagged FDA
cox2 COX-2 Activity Data
bagEarth Bagged Earth
pcaNNet.default Neural Networks with a Principal Component Step
roc Compute the points for an ROC curve
format.bagEarth Format 'bagEarth' objects
featurePlot Wrapper for Lattice Plotting of Predictor Variables
normalize.AffyBatch.normalize2Reference Quantile Normalization to a Reference Distribution
mdrr Multidrug Resistance Reversal (MDRR) Agent Data
as.table.confusionMatrix Save Confusion Table Results
print.train Print Method for the train Class
maxDissim Maximum Dissimilarity Sampling
predict.knnreg Predictions from k-Nearest Neighbors Regression Model
tecator Fat, Water and Protein Content of Maat Samples
filterVarImp Calculation of filter-based variable importance
postResample Calculates performance across resamples
BloodBrain Blood Brain Barrier Data
Alternate Affy Gene Expression Summary Methods. Generate Expression Values from Probes
predict.knn3 Predictions from k-Nearest Neighbors
createDataPartition Data Splitting functions
summary.bagEarth Summarize a bagged earth or FDA fit
knn3 k-Nearest Neighbour Classification
oneSE Selecting tuning Parameters
knnreg k-Nearest Neighbour Regression
sensitivity Calculate sensitivity, specificity and predictive values
pottery Pottery from Pre-Classical Sites in Italy
predict.train Extract predictions and class probabilities from train objects
findCorrelation Determine highly correlated variables
plot.varImp.train Plotting variable importance measures
panel.needle Needle Plot Lattice Panel
trainControl Control parameters for train
plsda Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
train Fit Predictive Models over Different Tuning Parameters
resampleHist Plot the resampling distribution of the model statistics
normalize2Reference Quantile Normalize Columns of a Matrix Based on a Reference Distribution
rfe Backwards Feature Selection Helper Functions
nearZeroVar Identification of near zero variance predictors
applyProcessing Data Processing on Predictor Variables (Deprecated)
histogram.train Lattice functions for plotting resampling results
plotObsVsPred Plot Observed versus Predicted Results in Regression and Classification Models
predictors List predictors used in the model
findLinearCombos Determine linear combinations in a matrix
oil Fatty acid composition of commercial oils
resampleSummary Summary of resampled performance estimates
caret-internal Internal Functions
dotPlot Create a dotplot of variable importance values
preProcess Pre-Processing of Predictors
confusionMatrix Create a confusion matrix
spatialSign Compute the multivariate spatial sign
varImp Calculation of variable importance for regression and classification models
predict.bagEarth Predicted values based on bagged Earth and FDA models
classDist Compute and predict the distances to class centroids
print.confusionMatrix Print method for confusionMatrix
plot.train Plot Method for the train Class
lattice.rfe Lattice functions for plotting resampling results of recursive feature selection
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