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arXiv Statistics ML By Ebrahim Khaled Ebrahim, Osama Abd El-Aziz Hussein, Ahmed El-Kotory

[DeepGOF] Where Does a Logistic Risk Model Fail? An Audited Neural Goodness-of-Fit Test for Model Development and External Validation

Read the original on arXiv Statistics ML →

DeepGOF-1 is a neural goodness‑of‑fit test for logistic regression that maps residuals over covariate ranks, scores them with a frozen convolutional network, and calibrates the score via parametric bootstrap. It achieves first‑order valid level, offers explicit local power, and outperforms traditional chi‑square and projection tests in simulations and real data, revealing missed interactions and guiding model repairs. In the SUPPORT study, it identified a U‑shaped risk pattern for blood pressure and respiratory rate that standard calibration curves obscured, and it ran in 13 seconds versus hours for competing methods.

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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