caret v4.31


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