# scikit-learn-contrib/boruta_py

Python implementations of the Boruta all-relevant feature selection method.

Repository: https://github.com/scikit-learn-contrib/boruta_py
Canonical: https://ross.abutalabs.com/products/boruta_py
Language: Python
License: BSD-3-Clause
License Family: permissive
Last push: 2025-11-13T04:00:49+00:00

## Health v2 (maintenance only)
Score: 46/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 52, release rhythm 8, longevity 100
- inputs: {"age_days": 3868, "days_push": 293, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1627, forks 264 (observed 2026-08-28T04:05:13.098667+00:00)

## What it is
A Python implementation of the Boruta all-relevant feature selection method, built on scikit-learn. It identifies all features carrying predictive information rather than just a minimal optimal subset, with a scikit-learn-compatible fit/transform interface.

## Use cases
- find all features relevant for prediction in a dataset
- feature selection before training a machine learning model
- understand which variables explain a phenomenon in my data
- rank features by importance using random forests
- select features for biological or genomic data analysis

## When to choose
- you want all relevant features, not just a minimal subset
- you work within the scikit-learn ecosystem
- you need feature importance ranking with statistical rigor

## When to avoid
- you need a minimal optimal feature subset for a specific classifier
- your data is too large for iterative random-forest fitting
- you need non-tree-based feature selection methods

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, data-science
- domain: machine-learning, data-science
- platform: python
- tags: feature-selection, scikit-learn, boruta, random-forest, all-relevant-feature-selection

## Member repositories
- scikit-learn-contrib/boruta_py (main) score 46

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:13.098667+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:48:25.869733+00:00, confidence not recorded.
  - readme: https://github.com/scikit-learn-contrib/boruta_py (fetched 2026-08-28T04:05:13.098667+00:00, sha 3c3198c6a1f9)
- Data as of 2026-08-30T08:39:29.467469+00:00.