caret v5.14-023

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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
BoxCoxTrans.default Box-Cox Transformations
classDist Compute and predict the distances to class centroids
GermanCredit German Credit Data
cars Kelly Blue Book resale data for 2005 model year GM cars
panel.needle Needle Plot Lattice Panel
pcaNNet.default Neural Networks with a Principal Component Step
preProcess Pre-Processing of Predictors
findLinearCombos Determine linear combinations in a matrix
predictors List predictors used in the model
resamples Collation and Visualization of Resampling Results
rfeControl Controlling the Feature Selection Algorithms
resampleHist Plot the resampling distribution of the model statistics
plotObsVsPred Plot Observed versus Predicted Results in Regression and Classification Models
print.train Print Method for the train Class
lift Lift Plot
diff.resamples Inferential Assessments About Model Performance
dotPlot Create a dotplot of variable importance values
format.bagEarth Format 'bagEarth' objects
resampleSummary Summary of resampled performance estimates
findCorrelation Determine highly correlated variables
as.table.confusionMatrix Save Confusion Table Results
icr.formula Independent Component Regression
confusionMatrix.train Estimate a Resampled Confusion Matrix
knnreg k-Nearest Neighbour Regression
modelLookup Descriptions Of Models Available in train()
xyplot.resamples Lattice Functions for Visualizing Resampling Results
predict.train Extract predictions and class probabilities from train objects
tecator Fat, Water and Protein Content of Meat Samples
dummyVars Create A Full Set of Dummy Variables
sbf Selection By Filtering (SBF)
lattice.rfe Lattice functions for plotting resampling results of recursive feature selection
bag.default A General Framework For Bagging
postResample Calculates performance across resamples
prcomp.resamples Principal Components Analysis of Resampling Results
BloodBrain Blood Brain Barrier Data
mdrr Multidrug Resistance Reversal (MDRR) Agent Data
featurePlot Wrapper for Lattice Plotting of Predictor Variables
knn3 k-Nearest Neighbour Classification
plot.train Plot Method for the train Class
bagFDA Bagged FDA
update.train Update and Re-fit a Model
calibration Probability Calibration Plot
normalize.AffyBatch.normalize2Reference Quantile Normalization to a Reference Distribution
createDataPartition Data Splitting functions
histogram.train Lattice functions for plotting resampling results
sensitivity Calculate sensitivity, specificity and predictive values
sbfControl Control Object for Selection By Filtering (SBF)
caretSBF Selection By Filtering (SBF) Helper Functions
caretFuncs Backwards Feature Selection Helper Functions
maxDissim Maximum Dissimilarity Sampling
caret-internal Internal Functions
predict.bagEarth Predicted values based on bagged Earth and FDA models
createGrid Tuning Parameter Grid
pottery Pottery from Pre-Classical Sites in Italy
print.confusionMatrix Print method for confusionMatrix
confusionMatrix Create a confusion matrix
dotplot.diff.resamples Lattice Functions for Visualizing Resampling Differences
Alternate Affy Gene Expression Summary Methods. Generate Expression Values from Probes
panel.lift2 Lattice Panel Functions for Lift Plots
spatialSign Compute the multivariate spatial sign
varImp Calculation of variable importance for regression and classification models
dhfr Dihydrofolate Reductase Inhibitors Data
plsda Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
normalize2Reference Quantile Normalize Columns of a Matrix Based on a Reference Distribution
plotClassProbs Plot Predicted Probabilities in Classification Models
segmentationData Cell Body Segmentation
nullModel Fit a simple, non-informative model
oil Fatty acid composition of commercial oils
predict.knn3 Predictions from k-Nearest Neighbors
plot.varImp.train Plotting variable importance measures
summary.bagEarth Summarize a bagged earth or FDA fit
oneSE Selecting tuning Parameters
nearZeroVar Identification of near zero variance predictors
trainControl Control parameters for train
bagEarth Bagged Earth
avNNet.default Neural Networks Using Model Averaging
cox2 COX-2 Activity Data
filterVarImp Calculation of filter-based variable importance
predict.knnreg Predictions from k-Nearest Neighbors Regression Model
rfe Backwards Feature Selection
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