caret v4.68

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