Boruta: Wrapper Algorithm for All Relevant Feature Selection (original) (raw)

An all relevant feature selection wrapper algorithm. It finds relevant features by comparing original attributes' importance with importance achievable at random, estimated using their permuted copies (shadows).

Version: 9.0.0
Imports: ranger
Suggests: mlbench, rFerns, randomForest, testthat, xgboost, survival
Published: 2025-07-22
DOI: 10.32614/CRAN.package.Boruta
Author: Miron Bartosz KursaORCID iD [aut, cre], Witold Remigiusz Rudnicki [aut]
Maintainer: Miron Bartosz Kursa <M.Kursa at icm.edu.pl>
BugReports: https://gitlab.com/mbq/Boruta/-/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://gitlab.com/mbq/Boruta/
NeedsCompilation: no
Citation: Boruta citation info
Materials:
In views: MachineLearning
CRAN checks: Boruta results

Documentation:

Reference manual: Boruta.html , <Boruta.pdf>
Vignettes: Boruta for those in a hurry (source, R code) Importance transdapters (source, R code)

Downloads:

Package source: Boruta_9.0.0.tar.gz
Windows binaries: r-devel: Boruta_9.0.0.zip, r-release: Boruta_9.0.0.zip, r-oldrel: Boruta_9.0.0.zip
macOS binaries: r-release (arm64): Boruta_9.0.0.tgz, r-oldrel (arm64): Boruta_9.0.0.tgz, r-release (x86_64): Boruta_9.0.0.tgz, r-oldrel (x86_64): Boruta_9.0.0.tgz
Old sources: Boruta archive

Reverse dependencies:

Reverse imports: CompositionalML, HDStIM, MSclassifR, multiclassPairs, SISIR
Reverse suggests: finnts, fuseMLR, mlr3filters, nestedcv

Linking:

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