Deselections for 2026.0

[ethanglaser]

Description

---

Checklist:

Completeness and readability

• I have commented my code, particularly in hard-to-understand areas.
• I have updated the documentation to reflect the changes or created a separate PR with updates and provided its number in the description, if necessary.
• Git commit message contains an appropriate signed-off-by string (see CONTRIBUTING.md for details).
• I have resolved any merge conflicts that might occur with the base branch.

Testing

• I have run it locally and tested the changes extensively.
• All CI jobs are green or I have provided justification why they aren't.
• I have extended testing suite if new functionality was introduced in this PR.

Performance

• I have measured performance for affected algorithms using scikit-learn_bench and provided at least a summary table with measured data, if performance change is expected.
• I have provided justification why performance and/or quality metrics have changed or why changes are not expected.
• I have extended the benchmarking suite and provided a corresponding scikit-learn_bench PR if new measurable functionality was introduced in this PR.

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.

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[david-cortes-intel]

Since 2026.0 is not an imminent release right now, perhaps we could instead try to investigate why a small test like this would time out.

[ethanglaser]

Agreed, and the test source is relatively straightforward: https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/ensemble/tests/test_forest.py#L1670. @Alexandr-Solovev would we expect there to be an issue with forest if max_samples is set to 2?