caret v4.77


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