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Computer vision: per label platt isotonic calibration

Fit a per-label probability calibrator on out-of-fold scores using Platt scaling (logistic regression on raw scores) and fall back to isotonic regression for labels where the logistic doesn't converge — pickle the dict of fitted calibrators and apply at inference for a small but free leaderboard lift on multi-label classification

Works with the context you provideVersion 1.0.0

Cv per label platt isotonic calibration

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  • **wenmin-wu/cv-per-label-platt-isotonic-calibration**: Fit a per-label probability calibrator on out-of-fold scores using Platt scaling (logistic regression on raw scores) and fall back to isotonic regression for labels where the logistic doesn't converge — pickle the dict of fitted calibrators and apply at inference for a small but free leaderboard lift on multi-label classification Read `references/wenmin-wu/skills/cv/per-label-platt-isotonic-calibration/SKILL.md`.

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