On the Unknowable Limits to Prediction
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866909487509536768 |
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| author | Yan, Jiani Rahal, Charles |
| author_facet | Yan, Jiani Rahal, Charles |
| contents | We propose a rigorous decomposition of predictive error, highlighting that not all 'irreducible' error is genuinely immutable. Many domains stand to benefit from iterative enhancements in measurement, construct validity, and modeling. Our approach demonstrates how apparently 'unpredictable' outcomes can become more tractable with improved data (across both target and features) and refined algorithms. By distinguishing aleatoric from epistemic error, we delineate how accuracy may asymptotically improve--though inherent stochasticity may remain--and offer a robust framework for advancing computational research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_19223 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | On the Unknowable Limits to Prediction Yan, Jiani Rahal, Charles Machine Learning Artificial Intelligence Computers and Society Methodology We propose a rigorous decomposition of predictive error, highlighting that not all 'irreducible' error is genuinely immutable. Many domains stand to benefit from iterative enhancements in measurement, construct validity, and modeling. Our approach demonstrates how apparently 'unpredictable' outcomes can become more tractable with improved data (across both target and features) and refined algorithms. By distinguishing aleatoric from epistemic error, we delineate how accuracy may asymptotically improve--though inherent stochasticity may remain--and offer a robust framework for advancing computational research. |
| title | On the Unknowable Limits to Prediction |
| topic | Machine Learning Artificial Intelligence Computers and Society Methodology |
| url | https://arxiv.org/abs/2411.19223 |