caret v4.43


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