Generalizability vs. Counterfactual Explainability Trade-Off
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866912402044354560 |
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| author | Veglianti, Fabiano Giorgi, Flavio Silvestri, Fabrizio Tolomei, Gabriele |
| author_facet | Veglianti, Fabiano Giorgi, Flavio Silvestri, Fabrizio Tolomei, Gabriele |
| contents | In this work, we investigate the relationship between model generalization and counterfactual explainability in supervised learning. We introduce the notion of $\varepsilon$-valid counterfactual probability ($\varepsilon$-VCP) -- the probability of finding perturbations of a data point within its $\varepsilon$-neighborhood that result in a label change. We provide a theoretical analysis of $\varepsilon$-VCP in relation to the geometry of the model's decision boundary, showing that $\varepsilon$-VCP tends to increase with model overfitting. Our findings establish a rigorous connection between poor generalization and the ease of counterfactual generation, revealing an inherent trade-off between generalization and counterfactual explainability. Empirical results validate our theory, suggesting $\varepsilon$-VCP as a practical proxy for quantitatively characterizing overfitting. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_23225 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generalizability vs. Counterfactual Explainability Trade-Off Veglianti, Fabiano Giorgi, Flavio Silvestri, Fabrizio Tolomei, Gabriele Machine Learning In this work, we investigate the relationship between model generalization and counterfactual explainability in supervised learning. We introduce the notion of $\varepsilon$-valid counterfactual probability ($\varepsilon$-VCP) -- the probability of finding perturbations of a data point within its $\varepsilon$-neighborhood that result in a label change. We provide a theoretical analysis of $\varepsilon$-VCP in relation to the geometry of the model's decision boundary, showing that $\varepsilon$-VCP tends to increase with model overfitting. Our findings establish a rigorous connection between poor generalization and the ease of counterfactual generation, revealing an inherent trade-off between generalization and counterfactual explainability. Empirical results validate our theory, suggesting $\varepsilon$-VCP as a practical proxy for quantitatively characterizing overfitting. |
| title | Generalizability vs. Counterfactual Explainability Trade-Off |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2505.23225 |