caret v4.30


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