caret v4.25


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