caret v4.64

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