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.

Version: 1.52.0
Imports: cluster, rpart, stats, nnet
Suggests: lattice, MASS, rpart.plot, ExplainPrediction
Published: 2018-01-04
Author: Marko Robnik-Sikonja and Petr Savicky
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_1.52.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: CORElearn_1.52.0.tgz
OS X Mavericks binaries: r-oldrel: CORElearn_1.52.0.tgz
Old sources: CORElearn archive

Reverse dependencies:

Reverse imports: AppliedPredictiveModeling, autoBagging, ExplainPrediction, miRNAss, semiArtificial


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