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arXiv Statistics ML By Ebrahim Khaled Ebrahim, Ahmed El-Kotory

EDGE: a closed-form directed test for the calibration of probabilistic binary classifiers

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The paper introduces EDGE, a closed‑form statistical test for assessing the calibration of probabilistic binary classifiers, specifically logistic regression. EDGE uses the same binned predicted‑versus‑observed table as a reliability diagram, projects standardized bin residuals onto a small basis of smooth calibration‑distortion shapes, and yields a null distribution that is a weighted sum of chi‑square variables. The method requires only a single pass over the data and a small eigendecomposition, avoiding refitting, resampling, or tuning, and remains robust in sparse or misspecified settings where other binned tests fail.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Statistics ML.

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