caret v4.72


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