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: 7.0.0
Imports: ranger
Suggests: mlbench, rFerns, randomForest, testthat, xgboost, survival
Published: 2020-05-21
Author: Miron Bartosz Kursa ORCID iD [aut, cre], Witold Remigiusz Rudnicki [aut]
Maintainer: Miron Bartosz Kursa <M.Kursa at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Citation: Boruta citation info
Materials: NEWS
In views: MachineLearning
CRAN checks: Boruta results


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


Package source: Boruta_7.0.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): Boruta_7.0.0.tgz, r-release (x86_64): Boruta_7.0.0.tgz, r-oldrel: Boruta_7.0.0.tgz
Old sources: Boruta archive

Reverse dependencies:

Reverse depends: hsdar
Reverse imports: immcp, multiclassPairs
Reverse suggests: fscaret, varrank


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