Analyzing Cost-Sensitive Surrogate Losses via $\mathcal{H}$-calibration
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912249818382336 |
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| author | Shah, Sanket Tambe, Milind Finocchiaro, Jessie |
| author_facet | Shah, Sanket Tambe, Milind Finocchiaro, Jessie |
| contents | This paper aims to understand whether machine learning models should be trained using cost-sensitive surrogates or cost-agnostic ones (e.g., cross-entropy). Analyzing this question through the lens of $\mathcal{H}$-calibration, we find that cost-sensitive surrogates can strictly outperform their cost-agnostic counterparts when learning small models under common distributional assumptions. Since these distributional assumptions are hard to verify in practice, we also show that cost-sensitive surrogates consistently outperform cost-agnostic surrogates on classification datasets from the UCI repository. Together, these make a strong case for using cost-sensitive surrogates in practice. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_19522 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Analyzing Cost-Sensitive Surrogate Losses via $\mathcal{H}$-calibration Shah, Sanket Tambe, Milind Finocchiaro, Jessie Machine Learning This paper aims to understand whether machine learning models should be trained using cost-sensitive surrogates or cost-agnostic ones (e.g., cross-entropy). Analyzing this question through the lens of $\mathcal{H}$-calibration, we find that cost-sensitive surrogates can strictly outperform their cost-agnostic counterparts when learning small models under common distributional assumptions. Since these distributional assumptions are hard to verify in practice, we also show that cost-sensitive surrogates consistently outperform cost-agnostic surrogates on classification datasets from the UCI repository. Together, these make a strong case for using cost-sensitive surrogates in practice. |
| title | Analyzing Cost-Sensitive Surrogate Losses via $\mathcal{H}$-calibration |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2502.19522 |