caret v4.15

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