Does the Model Say What the Data Says? A Simple Heuristic for Model Data Alignment

• URL: http://arxiv.org/abs/2511.21931v1
• Date: Wed, 26 Nov 2025 21:44:55 GMT
• Title: Does the Model Say What the Data Says? A Simple Heuristic for Model Data Alignment
• Authors: Henry Salgado, Meagan Kendall, Martine Ceberio,
• Abstract summary: We propose a framework to evaluate whether machine learning models align with the structure of the data they learn from.<n>Unlike existing interpretability methods that focus exclusively on explaining model behavior, our approach establishes a baseline derived directly from the data itself.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we propose a simple and computationally efficient framework to evaluate whether machine learning models align with the structure of the data they learn from; that is, whether \textit{the model says what the data says}. Unlike existing interpretability methods that focus exclusively on explaining model behavior, our approach establishes a baseline derived directly from the data itself. Drawing inspiration from Rubin's Potential Outcomes Framework, we quantify how strongly each feature separates the two outcome groups in a binary classification task, moving beyond traditional descriptive statistics to estimate each feature's effect on the outcome. By comparing these data-derived feature rankings against model-based explanations, we provide practitioners with an interpretable and model-agnostic method to assess model--data alignment.

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