scikit-learn

license

BSD-3-Clause(OSI)

source

public(github.com/scikit-learn/scikit-learn)

core features withheld

no — community-governed

BSD-3-Clause, and the whole library builds from the public repository. It is run as a consensus-governed community project; Probabl, which funds part of the work, sells support and services as its own offering rather than a withheld edition of the library.

• https://raw.githubusercontent.com/scikit-learn/scikit-learn/HEAD/COPYING recorded 2026-09-26

  COPYING: "BSD 3-Clause License", "Copyright (c) 2007-2026 The scikit-learn developers."

• https://raw.githubusercontent.com/scikit-learn/scikit-learn/HEAD/README.rst recorded 2026-09-26

  README: "scikit-learn is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license."; no paid tier or enterprise edition mentioned.

• https://scikit-learn.org/stable/governance.html recorded 2026-09-26

  Governance page: "This is a meritocratic, consensus-based community project."; footer: "scikit-learn is financially supported by Probabl and other institutions", linking to Probabl's "Enterprise-grade solutions and services".

• https://ungh.cc/repos/scikit-learn/scikit-learn/files/HEAD recorded 2026-09-26

  Repository tree of 1,837 paths with no ee, enterprise, commercial or pro directory.

Measured on monthly PyPI downloads of the scikit-learn package.

• https://pypistats.org/api/packages/scikit-learn/recent recorded 2026-09-26

  last_month 183,812,323 downloads of scikit-learn

scikit-learn is the reference toolkit of classical learners, with the pipelines, cross-validation and metrics to compare them. Choosing among them, tuning and ensembling are left to the user or to tools built on it, which is the work AutoGluon automates.

• https://scikit-learn.org/stable/user_guide.html recorded 2026-09-26

  User Guide index: "1. Supervised learning" (linear models, SVMs, nearest neighbors, Gaussian processes, naive Bayes, decision trees, "Ensembles: Gradient boosting, random forests, bagging, voting, stacking", neural network models); "2. Unsupervised learning"; "3. Model selection and evaluation"; "8. Dataset transformations".