caret v4.88


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