Does the Model Say What the Data Says? A Simple Heuristic for Model Data Alignment
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866918458301612032 |
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| author | Salgado, Henry Kendall, Meagan R. Ceberio, Martine |
| author_facet | Salgado, Henry Kendall, Meagan R. Ceberio, Martine |
| contents | In this work, we propose a simple and computationally efficient framework for evaluating whether machine learning models align with the structure of the data they learn from; that is, whether 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 with model-based explanations, we provide practitioners with an interpretable and model-agnostic method for assessing model-data alignment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_21931 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Does the Model Say What the Data Says? A Simple Heuristic for Model Data Alignment Salgado, Henry Kendall, Meagan R. Ceberio, Martine Machine Learning Artificial Intelligence In this work, we propose a simple and computationally efficient framework for evaluating whether machine learning models align with the structure of the data they learn from; that is, whether 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 with model-based explanations, we provide practitioners with an interpretable and model-agnostic method for assessing model-data alignment. |
| title | Does the Model Say What the Data Says? A Simple Heuristic for Model Data Alignment |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2511.21931 |