caret v6.0-24

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