# scikit-learn/scikit-learn

scikit-learn: machine learning in Python

Repository: https://github.com/scikit-learn/scikit-learn
Canonical: https://ross.abutalabs.com/products/scikit-learn
Homepage: https://scikit-learn.org
Language: Python
License: BSD-3-Clause
License Family: permissive
Topics: machine-learning, python, statistics, data-science, data-analysis
Last push: 2026-08-26T13:46:35+00:00

## Health v2 (maintenance only)
Score: 90/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 74, longevity 100
- inputs: {"age_days": 5860, "days_push": 7, "days_rel": 92, "gap_med": 89, "n_releases_24m": 8}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 67065, forks 27318 (observed 2026-08-28T04:12:20.800142+00:00)

## What it is
scikit-learn is a Python module for machine learning built on top of SciPy, providing classification, regression, clustering, and model evaluation tools. It is a mature, BSD-licensed open-source project maintained by a large community since 2007.

## Use cases
- train a classifier on tabular data
- fit a regression model in Python
- cluster unlabeled data
- evaluate model performance with cross-validation
- preprocess and scale features for ML
- build a machine learning pipeline

## When to choose
- you need classical ML (not deep learning) in Python
- you want a well-documented, stable, widely supported library
- you need consistent APIs for preprocessing, training, and evaluation

## When to avoid
- you need GPU-accelerated deep learning (use PyTorch or TensorFlow)
- you need distributed training at massive scale (use Spark MLlib)
- you need the latest research models not yet implemented

## Facets
- artifact type: library
- maturity: stable
- function: machine-learning, data-science, math
- domain: machine-learning, data-science
- platform: python, cross-platform
- tags: classification, regression, clustering, model-evaluation, numpy, scipy, algorithms

## Member repositories
- scikit-learn/scikit-learn (main) score 90

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:12:20.800142+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 2026-08-29T16:16:24.889544+00:00, confidence not recorded.
  - readme: https://github.com/scikit-learn/scikit-learn (fetched 2026-08-28T04:12:20.800142+00:00, sha 07d15d0ef025)
  - homepage: https://scikit-learn.org (fetched 2026-08-28T17:52:37.199386+00:00, sha 8a13d2a4d792)
- Data as of 2026-08-30T08:39:29.467469+00:00.