caret v5.15-023

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