caret v4.39


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