chasedehan / BoostARoota
A fast xgboost feature selection algorithm
Why this job: works in the areas we cover, but does not say which job it does
What it does
A fast, practical feature selection algorithm built on XGBoost — with support for other scikit-learn tree-based models too.
Get started
pip install boostaroota- Maintenance
- Active
- Last commit 4 months ago
- Stars
- 231
- On GitHub
- Licence
- MIT
- Allows business use
- Downloads for
- None listed
- No release files
- Latest version
- None yet
- No releases
- Built with
- Python
- 69% of the code
Is it looked after?
- Contributors
- Not stated
- Last 90 days
- Issues
- 100% closed
- 0 still open
- Pull requests
- 8 merged
- 0 waiting
- Releases
- None
- Never published
- No contributing guide
- No security policy
- No code of conduct
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More: languages, topics and project record
Languages
- Python 69%
- Jupyter Notebook 27%
- Shell 3%
- Makefile 1%
Topics
- machine-learning
- machine-learning-algorithms
- feature-selection
- xgboost-algorithm
- xgboost
- dimension-reduction
- algorithm
- boruta
- data-science
- datascientist
- datascience
- machinelearning
Project record
- JobOther AI tools
- StatusListed
- Commits47
- Forks36
- Watching13
- Stars in 28 days0
- CreatedAug 23, 2017
- First seen by usNov 7, 2026
- CheckedNov 8, 2026