caret v5.09-006


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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
confusionMatrix.train Estimate a Resampled Confusion Matrix
bag.default A General Framework For Bagging
aucRoc Compute the area under an ROC curve
classDist Compute and predict the distances to class centroids
icr.formula Independent Component Regression
caretSBF Selection By Filtering (SBF) Helper Functions
confusionMatrix Create a confusion matrix
dotplot.diff.resamples Lattice Functions for Visualizing Resampling Differences
bagFDA Bagged FDA
dhfr Dihydrofolate Reductase Inhibitors Data
avNNet.default Neural Networks Using Model Averaging
filterVarImp Calculation of filter-based variable importance
maxDissim Maximum Dissimilarity Sampling
predict.knnreg Predictions from k-Nearest Neighbors Regression Model
nullModel Fit a simple, non-informative model
sensitivity Calculate sensitivity, specificity and predictive values
diff.resamples Inferential Assessments About Model Performance
pcaNNet.default Neural Networks with a Principal Component Step
sbf Selection By Filtering (SBF)
findCorrelation Determine highly correlated variables
knnreg k-Nearest Neighbour Regression
summary.bagEarth Summarize a bagged earth or FDA fit
BloodBrain Blood Brain Barrier Data
as.table.confusionMatrix Save Confusion Table Results
sbfControl Control Object for Selection By Filtering (SBF)
predict.bagEarth Predicted values based on bagged Earth and FDA models
plotClassProbs Plot Predicted Probabilities in Classification Models
preProcess Pre-Processing of Predictors
tecator Fat, Water and Protein Content of Meat Samples
createDataPartition Data Splitting functions
oneSE Selecting tuning Parameters
resamples Collation and Visualization of Resampling Results
normalize2Reference Quantile Normalize Columns of a Matrix Based on a Reference Distribution
cox2 COX-2 Activity Data
rfeControl Controlling the Feature Selection Algorithms
plot.train Plot Method for the train Class
lift Lift Plot
bagEarth Bagged Earth
normalize.AffyBatch.normalize2Reference Quantile Normalization to a Reference Distribution
cars Kelly Blue Book resale data for 2005 model year GM cars
prcomp.resamples Principal Components Analysis of Resampling Results
featurePlot Wrapper for Lattice Plotting of Predictor Variables
knn3 k-Nearest Neighbour Classification
mdrr Multidrug Resistance Reversal (MDRR) Agent Data
panel.lift2 Lattice Panel Functions for Lift Plots
format.bagEarth Format 'bagEarth' objects
panel.needle Needle Plot Lattice Panel
pottery Pottery from Pre-Classical Sites in Italy
xyplot.resamples Lattice Functions for Visualizing Resampling Results
Alternate Affy Gene Expression Summary Methods. Generate Expression Values from Probes
findLinearCombos Determine linear combinations in a matrix
plsda Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
histogram.train Lattice functions for plotting resampling results
modelLookup Descriptions Of Models Available in train()
nearZeroVar Identification of near zero variance predictors
plotObsVsPred Plot Observed versus Predicted Results in Regression and Classification Models
varImp Calculation of variable importance for regression and classification models
segmentationData Cell Body Segmentation
caretFuncs Backwards Feature Selection Helper Functions
rfe Backwards Feature Selection
roc Compute the points for an ROC curve
print.confusionMatrix Print method for confusionMatrix
predict.knn3 Predictions from k-Nearest Neighbors
resampleHist Plot the resampling distribution of the model statistics
train Fit Predictive Models over Different Tuning Parameters
BoxCoxTrans.default Box-Cox Transformations
dotPlot Create a dotplot of variable importance values
dummyVars Create A Full Set of Dummy Variables
createGrid Tuning Parameter Grid
oil Fatty acid composition of commercial oils
resampleSummary Summary of resampled performance estimates
GermanCredit German Credit Data
plot.varImp.train Plotting variable importance measures
predict.train Extract predictions and class probabilities from train objects
lattice.rfe Lattice functions for plotting resampling results of recursive feature selection
spatialSign Compute the multivariate spatial sign
postResample Calculates performance across resamples
trainControl Control parameters for train
predictors List predictors used in the model
print.train Print Method for the train Class
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