Package: CORElearn 1.57.3.1

Marko Robnik-Sikonja

CORElearn: Classification, Regression and Feature Evaluation

A suite of machine learning algorithms written in C++ with the R interface contains several learning techniques for classification and regression. Predictive models include e.g., 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 the 'ExplainPrediction' package. This 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 feature selection or discretization of numeric attributes. The OrdEval algorithm and its visualization is 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.

Authors:Marko Robnik-Sikonja [aut, cre], Petr Savicky [aut]

CORElearn_1.57.3.1.tar.gz
CORElearn_1.57.3.1.tar.gz(r-4.5-noble)CORElearn_1.57.3.1.tar.gz(r-4.4-noble)
CORElearn_1.57.3.1.tgz(r-4.4-emscripten)CORElearn_1.57.3.1.tgz(r-4.3-emscripten)
CORElearn.pdf |CORElearn.html
CORElearn/json (API)

# Install 'CORElearn' in R:
install.packages('CORElearn', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

cppopenmp

5.85 score 3 stars 8 packages 228 scripts 5.4k downloads 8 mentions 52 exports 5 dependencies

Last updated 1 months agofrom:3558b0a7c5. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 05 2024
R-4.5-linux-x86_64OKDec 05 2024

Exports:allTestsapplyCalibrationapplyDiscretizationattrEvalcalibrateclassDataGenclassPrototypesCoreModelcvCoreModelcvGencvGenStratifieddestroyModelsdiscretizedisplaydisplay.CoreModelgatherFromListgetCoreModelgetRFsizesgetRpartModelinfoCoreintervalMidPointloadRFmodelEvalnoEqualRowsordDataGenordEvalparamCoreIOplot.CoreModelplot.ordEvalplotOrdEvalpredict.CoreModelpreparePlotprintOrdEvalregDataGenreliabilityPlotrfAttrEvalrfAttrEvalClusteringrfClusteringrfOOBrfOutliersrfProximitysaveRFtestClassPseudoRandomtestCoreAttrEvaltestCoreClasstestCoreNAtestCoreOrdEvaltestCoreRandtestCoreRegtestCoreRPORTtestTimeversionCore

Dependencies:clusternnetplotrixrpartrpart.plot

Readme and manuals

Help Manual

Help pageTopics
R port of CORElearnCORElearn-package CORElearn
Attribute evaluationattrEval
Test functions for manual usagetestClassPseudoRandom testTime
Calibration of probabilities according to the given prior.applyCalibration calibrate
Artificial data for testing classification algorithmsclassDataGen
The typical instances of each class - class prototypesclassPrototypes
Internal structures of CORElearn C++ partCORElearn-internal
Build a classification or regression modelCoreModel cvCoreModel
Cross-validation and stratified cross-validationcvGen cvGenStratified gatherFromList
Destroy single model or all CORElearn modelsdestroyModels
Discretization of numeric attributesapplyDiscretization discretize intervalMidPoint
Displaying decision and regression treesdisplay display.CoreModel
Conversion of model to a listgetCoreModel
Get sizes of the trees in RFgetRFsizes
Conversion of a CoreModel tree into a rpart.objectgetRpartModel
Description of parameters.help.Core helpCore
Description of certain CORElearn parametersinfoCore
Statistical evaluation of predictionsmodelEval
Number of equal rows in two data setsnoEqualRows
Artificial data for testing ordEval algorithmsordDataGen
Evaluation of ordered attributesOrdEval ordEval
Input/output of parameters from/to fileparamCoreIO
Visualization of CoreModel modelsplot.CoreModel
Visualization of ordEval resultsplot.ordEval plotOrdEval printOrdEval
Prediction using constructed modelpredict predict.CoreModel
Prepare graphics devicepreparePlot preparePlot.Core
Artificial data for testing regression algorithmsregDataGen
Plots reliability plot of probabilitiesreliabilityPlot
Attribute evaluation with random forestrfAttrEval rfAttrEvalClustering
Random forest based clusteringrfClustering
Out-of-bag performance estimation for random forestsrfOOB
Random forest based outlier detectionrfOutliers
A random forest based proximity functionrfProximity
Saves/loads random forests model to/from fileloadRF saveRF
Verification of the CORElearn installationallTests testCore testCoreAttrEval testCoreClass testCoreNA testCoreOrdEval testCoreRand testCoreReg testCoreRPORT
Package versionversionCore