caret v4.67

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