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Honest calibration assessment for binary outcome predictions

Timo Dimitriadis · Lutz Duembgen · Alexander Henzi · Marius Puke · Johanna Ziegel首次提交 2022-03-08 · 修订 2022-11-02 · v2arXiv预印本

二元结果概率预测的诚实校准评估AI 解读

Probability predictions from binary regressions or machine learning methods ought to be calibrated: If an event is predicted to occur with probability $x$ , it should materialize with approximately that frequency, which means that the so-called calibration curve $p(\cdot)$ should equal the identity, $p(x) = x$ for all $x$ in the unit interval. We propose honest calibration assessment based on novel confidence bands for the calibration curve, which are valid only subject to…Probability predictions from binary regressions or machine learning methods ought to be calibrated: If an event is predicted to occur with probability $x$, it should materialize with approximately that frequency, which means that the so-called calibration curve $p(\cdot)$ should equal the identity, $p(x) = x$ for all $x$ in the unit interval. We propose honest calibration assessment based on novel confidence bands for the calibration curve, which are valid only subject to…

机器学习与 DML 多重检验与同时推断

关联记录被引 1 次 ↗OpenAlex · 核验 2026-09-30

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