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.


Reference manual

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1.56.0 by Marko Robnik-Sikonja, 10 months ago

Browse source code at

Authors: Marko Robnik-Sikonja and Petr Savicky

Documentation:   PDF Manual  

Task views: Machine Learning & Statistical Learning

GPL-3 license

Imports cluster, stats, nnet, plotrix, rpart.plot

Suggests lattice, MASS, ExplainPrediction

Imported by AppliedPredictiveModeling, ExplainPrediction, QWDAP, autoBagging, miRNAss, semiArtificial, snap.

Suggested by mlquantify.

See at CRAN