CORElearn: Classification, Regression and Feature Evaluation
Version 1.50.3

A suite of machine learning algorithms written in C++ with 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 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 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.

Package details

AuthorMarko Robnik-Sikonja and Petr Savicky
Date of publication2017-03-28 15:27:04 UTC
Maintainer"Marko Robnik-Sikonja" <>
Package repositoryView on CRAN
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CORElearn documentation built on May 29, 2017, 7:17 p.m.