caret v4.45

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