caret v4.49


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