Boruta: Wrapper Algorithm for All Relevant Feature Selection

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: 8.0.0
Imports: ranger
Suggests: mlbench, rFerns, randomForest, testthat, xgboost, survival
Published: 2022-11-12
Author: Miron Bartosz Kursa ORCID 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: NEWS
In views: MachineLearning
CRAN checks: Boruta results

Documentation:

Reference manual: Boruta.pdf
Vignettes: Boruta for those in a hurry
Importance transdapters

Downloads:

Package source: Boruta_8.0.0.tar.gz
Windows binaries: r-devel: Boruta_8.0.0.zip, r-release: Boruta_8.0.0.zip, r-oldrel: Boruta_8.0.0.zip
macOS binaries: r-release (arm64): Boruta_8.0.0.tgz, r-oldrel (arm64): Boruta_8.0.0.tgz, r-release (x86_64): Boruta_8.0.0.tgz
Old sources: Boruta archive

Reverse dependencies:

Reverse imports: CompositionalML, HDStIM, multiclassPairs, SISIR
Reverse suggests: finnts, fscaret, nestedcv, varrank

Linking:

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