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 [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 |
Reference manual: | Boruta.pdf |
Vignettes: |
Boruta for those in a hurry |
Package source: | Boruta_7.0.0.tar.gz |
Windows binaries: | r-devel: Boruta_7.0.0.zip, r-release: Boruta_7.0.0.zip, r-oldrel: Boruta_7.0.0.zip |
macOS binaries: | r-release (arm64): Boruta_7.0.0.tgz, r-oldrel (arm64): Boruta_7.0.0.tgz, r-release (x86_64): Boruta_7.0.0.tgz, r-oldrel (x86_64): Boruta_7.0.0.tgz |
Old sources: | Boruta archive |
Reverse depends: | hsdar |
Reverse imports: | HDStIM, multiclassPairs, nestedcv |
Reverse suggests: | fscaret |
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