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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2508.14368 |
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| _version_ | 1866915591265189888 |
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| author | Burn, Ryan |
| author_facet | Burn, Ryan |
| contents | I develop an algorithm to produce the piecewise quadratic that computes leave-one-out cross-validation for the lasso as a function of its hyperparameter. The algorithm can be used to find exact hyperparameters that optimize leave-one-out cross-validation either globally or locally, and its practicality is demonstrated on real-world data sets. I also show how the algorithm can be modified to compute approximate leave-one-out cross-validation, making it suitable for larger data sets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_14368 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Evaluation and Optimization of Leave-one-out Cross-validation for the Lasso Burn, Ryan Machine Learning Computation I develop an algorithm to produce the piecewise quadratic that computes leave-one-out cross-validation for the lasso as a function of its hyperparameter. The algorithm can be used to find exact hyperparameters that optimize leave-one-out cross-validation either globally or locally, and its practicality is demonstrated on real-world data sets. I also show how the algorithm can be modified to compute approximate leave-one-out cross-validation, making it suitable for larger data sets. |
| title | Evaluation and Optimization of Leave-one-out Cross-validation for the Lasso |
| topic | Machine Learning Computation |
| url | https://arxiv.org/abs/2508.14368 |