caret v5.15-61


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