Repo Compare › Classical ML

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. Refresh with live data

The wide bar is that repository’s share of the largest value in its own row — half the length means half the number. The thin gauge beneath it is the same row rescaled from its smallest value to its largest, which makes a close gap such as 61k against 59k visible; on that gauge the row’s lowest value always sits at zero however big it is, so read it as a spread and never as a quantity. Bars compare repositories with each other and never one metric against another. Colour identifies the repository, matching the dot beside its name. On the two issue rows a longer bar means more open work, which is usually the opposite of better. The highlighted cell is 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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