caret v4.73


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