CORElearn: Classification, Regression and Feature Evaluation

This is a suite of machine learning algorithms written in C++ with R interface. It contains several machine learning model learning techniques in classification and regression, for example classification and regression trees with optional constructive induction and models in the leaves, random forests, kNN, naive Bayes, and locally weighted regression. All predictions obtained with these models can be explained and visualized with ExplainPrediction package. The package is especially strong in feature evaluation where it contains several variants of Relief algorithm and many impurity based attribute evaluation functions, e.g., Gini, information gain, MDL, and DKM. These methods can be used for example to discretize numeric attributes. Its additional feature is OrdEval algorithm and its visualization used for evaluation of data sets with ordinal features and class, enabling analysis according to the Kano model of customer satisfaction. Several algorithms support parallel multithreaded execution via OpenMP. The top-level documentation is reachable through ?CORElearn.

AuthorMarko Robnik-Sikonja and Petr Savicky with contributions from John Adeyanju Alao
Date of publication2016-07-25 20:39:07
Maintainer"Marko Robnik-Sikonja" <marko.robnik@fri.uni-lj.si>
LicenseGPL-3
Version1.48.0
http://lkm.fri.uni-lj.si/rmarko/software/

View on CRAN

Man pages

attrEval: Attribute evaluation

auxTest: Test functions for manual usage

calibrate: Calibration of probabilities according to the given prior.

classDataGen: Artificial data for testing classification algorithms

classPrototypes: The typical instances of each class - class prototypes

CORElearn-internal: Internal structures of CORElearn C++ part

CORElearn-package: R port of CORElearn

CoreModel: Build a classification or regression model

destroyModels: Destroy single or all CORElearn models

discretize: Discretization of numeric attributes

display.CoreModel: Displaying decision and regression trees

getCoreModel: Conversion of model to a list

getRFsizes: Get sizes of the trees in RF

getRpartModel: Conversion of a CoreModel tree into a rpart.object

helpCore: Description of parameters.

infoCore: Description of certain CORElearn parameters

modelEval: Statistical evaluation of predictions

noEqualRows: Number of equal rows in two data sets

ordDataGen: Artificial data for testing ordEval algorithms

ordEval: Evaluation of ordered attributes

paramCoreIO: Input/output of parameters from/to file

plot.CoreModel: Visualization of CoreModel models

plot.ordEval: Visualization of ordEval results

predict.CoreModel: Prediction using constructed model

preparePlot: Prepare graphics device

regDataGen: Artificial data for testing regression algorithms

rfAttrEval: Attribute evaluation with random forest

rfClustering: Random forest based clustering

rfOOB: Out-of-bag performance estimation for random forests

rfOutliers: Random forest based outlier detection

rfProximity: A random forest based proximity function

saveRF: Saves/loads random forests model to/from file

testCore: Verification of the CORElearn installation

versionCore: Package version

Functions

allTests Man page
applyDiscretization Man page
attrEval Man page
calibrate Man page
classDataGen Man page
classPrototypes Man page
CORElearn Man page
CORElearn-internal Man page
CORElearn-package Man page
CoreModel Man page
destroyModels Man page
discretize Man page
display Man page
display.CoreModel Man page
getCoreModel Man page
getRFsizes Man page
getRpartModel Man page
helpCore Man page
help.Core Man page
infoCore Man page
intervalMidPoint Man page
loadRF Man page
modelEval Man page
noEqualRows Man page
ordDataGen Man page
ordEval Man page
paramCoreIO Man page
plot.CoreModel Man page
plot.ordEval Man page
plotOrdEval Man page
predict Man page
predict.CoreModel Man page
preparePlot Man page
preparePlot.Core Man page
printOrdEval Man page
regDataGen Man page
rfAttrEval Man page
rfAttrEvalClustering Man page
rfClustering Man page
rfOOB Man page
rfOutliers Man page
rfProximity Man page
saveRF Man page
testClassPseudoRandom Man page
testCore Man page
testCoreAttrEval Man page
testCoreClass Man page
testCoreNA Man page
testCoreOrdEval Man page
testCoreRand Man page
testCoreReg Man page
testCoreRPORT Man page
testTime Man page
versionCore Man page

Files

CORElearn
CORElearn/src
CORElearn/src/binpart.h
CORElearn/src/cost.cpp
CORElearn/src/mstring.cpp
CORElearn/src/rfUtil.cpp
CORElearn/src/estimator.h
CORElearn/src/exprReg.cpp
CORElearn/src/binnodeReg.h
CORElearn/src/Rfront.h
CORElearn/src/ftree.cpp
CORElearn/src/binarizeReg.cpp
CORElearn/src/random.cpp
CORElearn/src/rrelieff.cpp
CORElearn/src/rfUtil.h
CORElearn/src/bintree.cpp
CORElearn/src/rfVisualFront.cpp
CORElearn/src/dataStore.cpp
CORElearn/src/frontend.h
CORElearn/src/options.h
CORElearn/src/binnodeReg.cpp
CORElearn/src/nrutil.h
CORElearn/src/error.h
CORElearn/src/printUtil.cpp
CORElearn/src/relieff.cpp
CORElearn/src/estCommon.cpp
CORElearn/src/general.h
CORElearn/src/mathutil.h
CORElearn/src/binpart.cpp
CORElearn/src/options.cpp
CORElearn/src/binnode.h
CORElearn/src/random.h
CORElearn/src/trutilReg.cpp
CORElearn/src/mlist.h
CORElearn/src/new_new.h
CORElearn/src/constrct.h
CORElearn/src/kdtree.h
CORElearn/src/kdtree.cpp
CORElearn/src/frontend.cpp
CORElearn/src/bintreeReg.cpp
CORElearn/src/binnode.cpp
CORElearn/src/Rconvert.cpp
CORElearn/src/estimatorReg.cpp
CORElearn/src/rndforest.cpp
CORElearn/src/utils.h
CORElearn/src/bintreeReg.h
CORElearn/src/exprReg.h
CORElearn/src/utils.cpp
CORElearn/src/error.cpp
CORElearn/src/treenode.cpp
CORElearn/src/expr.h
CORElearn/src/ftree.h
CORElearn/src/pruneReg.cpp
CORElearn/src/Makevars.in
CORElearn/src/expr.cpp
CORElearn/src/new_new.cpp
CORElearn/src/bintree.h
CORElearn/src/calibrate.cpp
CORElearn/src/mathutil.cpp
CORElearn/src/regtree.cpp
CORElearn/src/printUtil.h
CORElearn/src/treenodeReg.cpp
CORElearn/src/modelReg.cpp
CORElearn/src/platform_unix.h
CORElearn/src/model.cpp
CORElearn/src/estimatorReg.h
CORElearn/src/constrctReg.cpp
CORElearn/src/menu.h
CORElearn/src/contain.h
CORElearn/src/rfVisual.cpp
CORElearn/src/rndforest.h
CORElearn/src/constrctReg.h
CORElearn/src/estOrdAttr.cpp
CORElearn/src/prune.cpp
CORElearn/src/c45read.cpp
CORElearn/src/Makevars.win
CORElearn/src/dataStore.h
CORElearn/src/mstring.h
CORElearn/src/c45read.h
CORElearn/src/binarize.cpp
CORElearn/src/calibrate.h
CORElearn/src/estimator.cpp
CORElearn/src/nrutil.cpp
CORElearn/src/trutil.cpp
CORElearn/src/regtree.h
CORElearn/src/platform_win.h
CORElearn/src/platform.h
CORElearn/src/rfRegularize.cpp
CORElearn/src/menu.cpp
CORElearn/src/constrct.cpp
CORElearn/src/Rfront.cpp
CORElearn/NAMESPACE
CORElearn/R
CORElearn/R/ordEval.R CORElearn/R/init.R CORElearn/R/dataGenerator.R CORElearn/R/util.R CORElearn/R/testCore.R CORElearn/R/rfVisualize.R CORElearn/R/Rinterface.R
CORElearn/MD5
CORElearn/DESCRIPTION
CORElearn/configure
CORElearn/ChangeLog
CORElearn/man
CORElearn/man/regDataGen.Rd CORElearn/man/CORElearn-internal.Rd CORElearn/man/CoreModel.Rd CORElearn/man/plot.CoreModel.Rd CORElearn/man/rfProximity.Rd CORElearn/man/getCoreModel.Rd CORElearn/man/getRpartModel.Rd CORElearn/man/infoCore.Rd CORElearn/man/testCore.Rd CORElearn/man/paramCoreIO.Rd CORElearn/man/destroyModels.Rd CORElearn/man/helpCore.Rd CORElearn/man/discretize.Rd CORElearn/man/noEqualRows.Rd CORElearn/man/rfClustering.Rd CORElearn/man/ordDataGen.Rd CORElearn/man/display.CoreModel.Rd CORElearn/man/modelEval.Rd CORElearn/man/classDataGen.Rd CORElearn/man/saveRF.Rd CORElearn/man/ordEval.Rd CORElearn/man/predict.CoreModel.Rd CORElearn/man/auxTest.Rd CORElearn/man/CORElearn-package.Rd CORElearn/man/versionCore.Rd CORElearn/man/rfOutliers.Rd CORElearn/man/getRFsizes.Rd CORElearn/man/attrEval.Rd CORElearn/man/rfAttrEval.Rd CORElearn/man/classPrototypes.Rd CORElearn/man/plot.ordEval.Rd CORElearn/man/rfOOB.Rd CORElearn/man/calibrate.Rd CORElearn/man/preparePlot.Rd

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