caret v4.57


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