caret v5.15-044


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