caret v4.17


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