CRAN/E | CORElearn

CORElearn

Classification, Regression and Feature Evaluation

Installation

About

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.

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

Key Metrics

Version 1.57.3
Published 2022-11-18 519 days ago
Needs compilation? yes
License GPL-3
CRAN checks CORElearn results

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Maintainer

Maintainer

"Marko Robnik-Sikonja"

marko.robnik@fri.uni-lj.si

Authors

Marko Robnik-Sikonja
Petr Savicky

Material

ChangeLog
Reference manual
Package source

In Views

MachineLearning

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

CORElearn archive

Imports

cluster
stats
nnet
plotrix
rpart.plot

Suggests

lattice
MASS
ExplainPrediction

Reverse Imports

AppliedPredictiveModeling
autoBagging
ExplainPrediction
miRNAss
nestedcv
QWDAP
semiArtificial
SISIR
snap

Reverse Suggests

familiar
mlquantify