Man pages for CORElearn
Classification, Regression and Feature Evaluation

attrEvalAttribute evaluation
auxTestTest functions for manual usage
calibrateCalibration of probabilities according to the given prior.
classDataGenArtificial data for testing classification algorithms
classPrototypesThe typical instances of each class - class prototypes
CORElearn-internalInternal structures of CORElearn C++ part
CORElearn-packageR port of CORElearn
CoreModelBuild a classification or regression model
cvGenCross-validation and stratified cross-validation
destroyModelsDestroy single model or all CORElearn models
discretizeDiscretization of numeric attributes
display.CoreModelDisplaying decision and regression trees
getCoreModelConversion of model to a list
getRFsizesGet sizes of the trees in RF
getRpartModelConversion of a CoreModel tree into a rpart.object
helpCoreDescription of parameters.
infoCoreDescription of certain CORElearn parameters
modelEvalStatistical evaluation of predictions
noEqualRowsNumber of equal rows in two data sets
ordDataGenArtificial data for testing ordEval algorithms
ordEvalEvaluation of ordered attributes
paramCoreIOInput/output of parameters from/to file
plot.CoreModelVisualization of CoreModel models
plot.ordEvalVisualization of ordEval results
predict.CoreModelPrediction using constructed model
preparePlotPrepare graphics device
regDataGenArtificial data for testing regression algorithms
reliabiltyPlotPlots reliability plot of probabilities
rfAttrEvalAttribute evaluation with random forest
rfClusteringRandom forest based clustering
rfOOBOut-of-bag performance estimation for random forests
rfOutliersRandom forest based outlier detection
rfProximityA random forest based proximity function
saveRFSaves/loads random forests model to/from file
testCoreVerification of the CORElearn installation
versionCorePackage version
CORElearn documentation built on Nov. 18, 2022, 5:08 p.m.