Newest papers

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

Jiani Yan, Pengyun Wang, Klint Kanopka, Ben Domingue, Charles Rahal · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.29960 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

准确性仅仅提供了关于哪些特性或模型组件提高概率匹配,哪些结果受益,或培训期间如何适应变化的信息很少。我们将InterModel Vigorish(IMV)引入机器智能空间,以表示匹配的差异,作为对参考模型的预期投注优势。SHAP-IMV通过Shapley重量子组比较将收益归因于特性,Multi-IMV评估一对一休息和对一类歧视,而Ablate-IMV通过抽象评估建筑组件和功能组件。我们通过使用表数据,图像,传感器序列和图表对十个随机种子进行评估。功能排名因估计而异。高对一休息的准确性可以伴随着零度的微不足的工作改善,而抽象可在固定预

Machine translation (Meta M2M100); the English original follows.

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 applications over ten random seeds using tabular data, images, sensor sequences, and graphs. Feature rankings vary across estimators. High one-vs-rest accuracy can accompany negligible improvement over a null, while ablation can preserve or improve fit under a fixed training budget. On six graph-classification benchmarks, we time-profile IMV over 200 training epochs, reporting seed means and ranges. The open-source imvpy library and accompanying notebooks provide code to replicate our work and to use the IMV more widely.

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Topics: Machine learning, Artificial intelligence, Computer science, Pairwise comparison, Feature (linguistics), Replicate

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