caret v4.27


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