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CORElearn  

Classification, Regression and Feature Evaluation
View on CRAN: Click here


Download and install CORElearn package within the R console
Install from CRAN:
install.packages("CORElearn")

Install from Github:
library("remotes")
install_github("cran/CORElearn")

Install by package version:
library("remotes")
install_version("CORElearn", "1.57.3.1")



Attach the package and use:
library("CORElearn")
Maintained by
Marko Robnik-Sikonja
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2009-09-20
Latest Update: 2022-11-18
Description:
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.
How to cite:
Marko Robnik-Sikonja (2009). CORElearn: Classification, Regression and Feature Evaluation. R package version 1.57.3.1, https://cran.r-project.org/web/packages/CORElearn. Accessed 22 Dec. 2024.
Previous versions and publish date:
0.9.22 (2009-09-20 22:07), 0.9.24 (2009-12-06 11:29), 0.9.25 (2010-01-08 09:08), 0.9.26 (2010-01-11 08:42), 0.9.28 (2010-09-03 09:31), 0.9.29 (2010-09-08 08:44), 0.9.30 (2010-09-14 09:36), 0.9.32 (2010-12-01 15:29), 0.9.33 (2011-03-26 16:43), 0.9.34 (2011-04-04 09:36), 0.9.35 (2011-08-19 09:02), 0.9.36 (2012-01-03 17:20), 0.9.37 (2012-01-17 15:11), 0.9.39 (2012-01-28 06:02), 0.9.40 (2012-07-10 07:46), 0.9.41 (2013-01-04 12:07), 0.9.42 (2013-10-18 10:24), 0.9.43 (2014-05-12 07:51), 0.9.44 (2014-12-24 06:22), 0.9.45 (2015-01-27 10:08), 0.9.46 (2015-06-03 19:06), 1.47.1 (2015-09-04 07:35), 1.48.0 (2016-07-25 20:39), 1.50.1 (2017-03-26 23:02), 1.50.2 (2017-03-28 10:56), 1.50.3 (2017-03-28 17:27), 1.51.2 (2017-08-08 16:00), 1.52.0 (2018-01-04 16:46), 1.52.1 (2018-04-02 16:31), 1.53.1 (2018-09-29 12:30), 1.54.2 (2020-02-08 11:20), 1.56.0 (2021-03-23 08:50), 1.57.1 (2022-11-06 16:10), 1.57.2 (2022-11-16 13:11), 1.57.3 (2022-11-18 15:10)
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