Stacked conformal prediction
Fuente:
arXiv
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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866911551657607168 |
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| author | F, Paulo C. Marques |
| author_facet | F, Paulo C. Marques |
| contents | We consider a method for conformalizing a stacked ensemble of predictive models, showing that the potentially simple form of the meta-learner at the top of the stack enables a procedure with manageable computational cost that achieves approximate marginal validity without requiring the use of a separate calibration sample. Empirical results indicate that the method compares favorably to a standard inductive alternative. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_12578 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Stacked conformal prediction F, Paulo C. Marques Machine Learning We consider a method for conformalizing a stacked ensemble of predictive models, showing that the potentially simple form of the meta-learner at the top of the stack enables a procedure with manageable computational cost that achieves approximate marginal validity without requiring the use of a separate calibration sample. Empirical results indicate that the method compares favorably to a standard inductive alternative. |
| title | Stacked conformal prediction |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2505.12578 |