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ENH Change default strategy='quantile' in calibration_curve#33908

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antoinebaker wants to merge 15 commits into
scikit-learn:mainfrom
antoinebaker:binning_strategy_calibration_curve
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ENH Change default strategy='quantile' in calibration_curve#33908
antoinebaker wants to merge 15 commits into
scikit-learn:mainfrom
antoinebaker:binning_strategy_calibration_curve

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@antoinebaker

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What does this implement/fix? Explain your changes.

Change the default strategy from 'uniform' to 'quantile' in calibration_curve, CalibrationDisplay.from_predictions and CalibrationDisplay.from_estimator.

Any other comments?

When estimating the calibration curve by binning, the quality of the estimate (how close it is to the true calibration curve) depends crucially on
1a. the number of bins, it seems that n_bins=n**(1/3) is best, this is tackled by #33856
1b. the binning strategy, it seems that "quantile" is better, see Roelofs et al or Nixon et al

@lorentzenchr

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@github-actions github-actions Bot added the CI:Linter failure The linter CI is failing on this PR label Jun 18, 2026
@github-actions github-actions Bot removed the CI:Linter failure The linter CI is failing on this PR label Jun 18, 2026
@antoinebaker antoinebaker moved this to In progress in Labs Jun 19, 2026
@antoinebaker antoinebaker marked this pull request as ready for review June 22, 2026 08:34
Comment thread sklearn/metrics/_plot/tests/test_common_curve_display.py Outdated
antoinebaker and others added 2 commits July 15, 2026 14:41
Co-authored-by: Christian Lorentzen <lorentzen.ch@gmail.com>
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