Mastering Model Fit: Applications of the InterModel Vigorish for Interpretable Machine Learning

Jiani Yan, Pengyun Wang, Klint Kanopka, Ben Domingue, Charles Rahal · 2026 · arXiv

DOI: 10.48550/arXiv.2610.29960 · open-access PDF

Abstract

Accuracy alone gives little information about which features or model components improve probabilistic fit, which outcomes benefit, or how fit changes during training. We introduce the InterModel Vigorish (IMV) into the Machine Intelligence space in order to express differences in fit as an expected betting advantage over a reference model. SHAP-IMV attributes gains to features through Shapley-weighted subset comparisons, Multi-IMV evaluates one-vs-rest and pairwise class discrimination, and Ablate-IMV assesses architectural components and feature groups by ablation. We evaluate these applicat

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