caret v6.0-29

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