caret v5.07-005


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