caret v5.07-001

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