caret v5.09-012


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