caret v5.11-06

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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
confusionMatrix.train Estimate a Resampled Confusion Matrix
classDist Compute and predict the distances to class centroids
lattice.rfe Lattice functions for plotting resampling results of recursive feature selection
findLinearCombos Determine linear combinations in a matrix
dhfr Dihydrofolate Reductase Inhibitors Data
BloodBrain Blood Brain Barrier Data
bag.default A General Framework For Bagging
featurePlot Wrapper for Lattice Plotting of Predictor Variables
lift Lift Plot
histogram.train Lattice functions for plotting resampling results
knnreg k-Nearest Neighbour Regression
BoxCoxTrans.default Box-Cox Transformations
postResample Calculates performance across resamples
panel.needle Needle Plot Lattice Panel
plot.varImp.train Plotting variable importance measures
createGrid Tuning Parameter Grid
spatialSign Compute the multivariate spatial sign
predict.train Extract predictions and class probabilities from train objects
train Fit Predictive Models over Different Tuning Parameters
plotClassProbs Plot Predicted Probabilities in Classification Models
pcaNNet.default Neural Networks with a Principal Component Step
confusionMatrix Create a confusion matrix
caret-internal Internal Functions
normalize.AffyBatch.normalize2Reference Quantile Normalization to a Reference Distribution
plsda Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
caretSBF Selection By Filtering (SBF) Helper Functions
summary.bagEarth Summarize a bagged earth or FDA fit
mdrr Multidrug Resistance Reversal (MDRR) Agent Data
createDataPartition Data Splitting functions
avNNet.default Neural Networks Using Model Averaging
dotPlot Create a dotplot of variable importance values
sbfControl Control Object for Selection By Filtering (SBF)
Alternate Affy Gene Expression Summary Methods. Generate Expression Values from Probes
dummyVars Create A Full Set of Dummy Variables
plot.train Plot Method for the train Class
varImp Calculation of variable importance for regression and classification models
diff.resamples Inferential Assessments About Model Performance
nullModel Fit a simple, non-informative model
caretFuncs Backwards Feature Selection Helper Functions
xyplot.resamples Lattice Functions for Visualizing Resampling Results
predict.bagEarth Predicted values based on bagged Earth and FDA models
rfeControl Controlling the Feature Selection Algorithms
resamples Collation and Visualization of Resampling Results
sbf Selection By Filtering (SBF)
print.train Print Method for the train Class
predict.knnreg Predictions from k-Nearest Neighbors Regression Model
segmentationData Cell Body Segmentation
resampleSummary Summary of resampled performance estimates
tecator Fat, Water and Protein Content of Meat Samples
plotObsVsPred Plot Observed versus Predicted Results in Regression and Classification Models
maxDissim Maximum Dissimilarity Sampling
predictors List predictors used in the model
roc Compute the points for an ROC curve
normalize2Reference Quantile Normalize Columns of a Matrix Based on a Reference Distribution
trainControl Control parameters for train
as.table.confusionMatrix Save Confusion Table Results
bagEarth Bagged Earth
format.bagEarth Format 'bagEarth' objects
prcomp.resamples Principal Components Analysis of Resampling Results
GermanCredit German Credit Data
preProcess Pre-Processing of Predictors
bagFDA Bagged FDA
sensitivity Calculate sensitivity, specificity and predictive values
cars Kelly Blue Book resale data for 2005 model year GM cars
filterVarImp Calculation of filter-based variable importance
icr.formula Independent Component Regression
panel.lift2 Lattice Panel Functions for Lift Plots
pottery Pottery from Pre-Classical Sites in Italy
print.confusionMatrix Print method for confusionMatrix
oneSE Selecting tuning Parameters
rfe Backwards Feature Selection
findCorrelation Determine highly correlated variables
dotplot.diff.resamples Lattice Functions for Visualizing Resampling Differences
predict.knn3 Predictions from k-Nearest Neighbors
knn3 k-Nearest Neighbour Classification
oil Fatty acid composition of commercial oils
aucRoc Compute the area under an ROC curve
nearZeroVar Identification of near zero variance predictors
resampleHist Plot the resampling distribution of the model statistics
cox2 COX-2 Activity Data
modelLookup Descriptions Of Models Available in train()
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