caret v5.07-024


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