# jundongl/scikit-feature

open-source feature selection repository in python

Repository: https://github.com/jundongl/scikit-feature
Canonical: https://ross.abutalabs.com/products/scikit-feature
Language: Python
License: GPL-2.0
License Family: copyleft
Last push: 2024-07-11T09:07:01+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3953, "days_push": 783, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1583, forks 438 (observed 2026-08-28T04:05:07.241882+00:00)

## What it is
scikit-feature is an open-source Python feature selection library built on scikit-learn, NumPy, and SciPy. It implements around 40 popular feature selection algorithms, including traditional, structural, and streaming methods, for research and applied use.

## Use cases
- select the most informative features from a high-dimensional dataset
- reduce dimensionality before training a machine learning model
- compare different feature selection algorithms empirically
- apply streaming feature selection to evolving data
- find relevant features for a classification or regression task
- implement and benchmark new feature selection research

## When to choose
- you need a broad collection of ready-to-use feature selection algorithms in Python
- you are doing academic research or comparative studies on feature selection
- your stack already uses scikit-learn, NumPy, and SciPy

## When to avoid
- you need actively maintained code with recent bug fixes and modern packaging
- you only need simple built-in feature selection, which scikit-learn already provides
- you require commercial-friendly licensing, since it is GPL-2.0

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, data-science
- domain: machine-learning, data-science
- platform: python, windows
- tags: feature-selection, scikit-learn, dimensionality-reduction, linux, macos

## Member repositories
- jundongl/scikit-feature (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:07.241882+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 2026-08-30T03:56:00.868215+00:00, confidence not recorded.
  - readme: https://github.com/jundongl/scikit-feature (fetched 2026-08-28T04:05:07.241882+00:00, sha 619adec9005d)
- Data as of 2026-08-30T08:39:29.467469+00:00.