Feature Selection with the Boruta Package

Miron B. Kursa and Witold R. Rudnicki

Journal of Statistical Software, 2010, vol. 036, issue i11

Abstract: This article describes a R package Boruta, implementing a novel feature selection algorithm for finding emph{all relevant variables}. The algorithm is designed as a wrapper around a Random Forest classification algorithm. It iteratively removes the features which are proved by a statistical test to be less relevant than random probes. The Boruta package provides a convenient interface to the algorithm. The short description of the algorithm and examples of its application are presented.

Date: 2010-09-16
References: View complete reference list from CitEc
Citations: View citations in EconPapers (133)

Downloads: (external link)
https://www.jstatsoft.org/index.php/jss/article/view/v036i11/v36i11.pdf
https://www.jstatsoft.org/index.php/jss/article/do ... 11/Boruta_1.3.tar.gz
https://www.jstatsoft.org/index.php/jss/article/do ... ile/v036i11/v36i11.R

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:jss:jstsof:v:036:i11

DOI: 10.18637/jss.v036.i11

Access Statistics for this article

Journal of Statistical Software is currently edited by Bettina Grün, Edzer Pebesma and Achim Zeileis

More articles in Journal of Statistical Software from Foundation for Open Access Statistics
Bibliographic data for series maintained by Christopher F. Baum ().