scikit-learn vs XGBoost vs LightGBM
A side-by-side look at how actively scikit-learn, XGBoost and LightGBM are developed on GitHub — popularity, contributor base, commit activity and how quickly issues and pull requests move.
What the numbers say
- scikit-learn is the most-starred of the 3, with 67k stars — about 3.6× LightGBM's 19k.
- Over the 52 weeks to the snapshot, scikit-learn recorded the most commits (1.1k), against 177 for LightGBM.
- scikit-learn has closed or merged the most pull requests in its lifetime: 21k.
- Relative to its popularity, XGBoost carries the lightest open-issue load: 14.0 open issues per 1,000 stars, versus 23.7 for LightGBM.
Head-to-head
Snapshot taken 30 September 2026.
| Metric | scikit-learn Active | XGBoost Active | LightGBM Active |
|---|---|---|---|
| Stars | 67k | 29k | 19k |
| Forks | 27k | 8.9k | 4.1k |
| Contributors | 3.6k | 715 | 358 |
| Commits, last 52 weeks | 1.1k | 496 | 177 |
| Open issues | 1.5k | 404 | 447 |
| Open pull requests | 607 | 38 | 89 |
| Closed / merged PRs | 21k | 6.8k | 3.7k |
| Watchers | 2.1k | 885 | 422 |
| Last push | 30 Sep 2026 | 30 Sep 2026 | 29 Sep 2026 |
| Latest release | 1.9.1 (Sep 2026) | v3.4.2 (Sep 2026) | v4.7.0 (Jul 2026) |
| Main language | Python | C++ | C++ |
| Licence | BSD-3-Clause | Apache-2.0 | MIT |
| Created | Aug 2010 | Feb 2014 | Aug 2016 |
Bold marks the highest value in a row. “Active / Slowing / Dormant” is a simple heuristic from how recently code was pushed and open issues per star — a starting point, not a verdict. Stars measure popularity, not quality.
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