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.


News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("CORElearn")

1.53.1 by Marko Robnik-Sikonja, 7 months ago


http://lkm.fri.uni-lj.si/rmarko/software/


Browse source code at https://github.com/cran/CORElearn


Authors: Marko Robnik-Sikonja and Petr Savicky


Documentation:   PDF Manual  


Task views: Machine Learning & Statistical Learning


GPL-3 license


Imports cluster, rpart, stats, nnet

Suggests lattice, MASS, rpart.plot, ExplainPrediction


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


See at CRAN