ThomasBury / arfs
All Relevant Feature Selection
Why this job: works in the areas we cover, but does not say which job it does
What it does
All relevant feature selection aims to find predictors carrying information about a response, including redundant predictors. These implementations compare fitted model importances with shuffled features. Results depend on the estimator, importance method and shadow threshold; the comparisons do not establish calibrated false-discovery control.
Get started
No single install command. Read the steps in the README (opens in new tab)
- Maintenance
- Active
- Last commit 22 days ago
- Stars
- 145
- On GitHub
- Licence
- MIT
- Allows business use
- Downloads for
- None listed
- No release files
- Latest version
- 4.0.1
- Released 27 days ago
- Built with
- Python
- 100% of the code
Is it looked after?
- Contributors
- Not stated
- Last 90 days
- Issues
- 98% closed
- 1 still open
- Pull requests
- 36 merged
- 0 waiting
- Releases
- 43
- 2 in the last year
- No contributing guide
- No security policy
- No code of conduct
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More: languages, topics and project record
Languages
- Python 100%
Topics
- allrelevant
- autobinning
- boruta
- discretization-algorithm
- feature-engineering
- feature-selection
- lightgbm
- machine-learning
- mrmr
- python
- shadow-features
Project record
- JobOther AI tools
- StatusListed
- Commits612
- Forks16
- Watching3
- Stars in 28 days0
- CreatedNov 27, 2020
- First seen by usNov 7, 2026
- CheckedNov 8, 2026