caret v4.48


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