caret v5.04-007

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