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.

Version: 1.48.0
Imports: cluster, rpart, stats
Suggests: lattice, MASS, rpart.plot, ExplainPrediction
Published: 2016-07-25
Author: Marko Robnik-Sikonja and Petr Savicky with contributions from John Adeyanju Alao
Maintainer: "Marko Robnik-Sikonja" <marko.robnik at>
License: GPL-3
NeedsCompilation: yes
Materials: ChangeLog
In views: MachineLearning
CRAN checks: CORElearn results


Reference manual: CORElearn.pdf
Package source: CORElearn_1.48.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X Mavericks binaries: r-release: CORElearn_1.48.0.tgz, r-oldrel: CORElearn_1.48.0.tgz
Old sources: CORElearn archive

Reverse dependencies:

Reverse depends: ExplainPrediction
Reverse imports: AppliedPredictiveModeling, semiArtificial


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