caret v5.17-7


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