Building Trees for Probabilistic Prediction via Scoring Rules
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866914840610603008 |
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| author | Shashaani, Sara Surer, Ozge Plumlee, Matthew Guikema, Seth |
| author_facet | Shashaani, Sara Surer, Ozge Plumlee, Matthew Guikema, Seth |
| contents | Decision trees built with data remain in widespread use for nonparametric prediction. Predicting probability distributions is preferred over point predictions when uncertainty plays a prominent role in analysis and decision-making. We study modifying a tree to produce nonparametric predictive distributions. We find the standard method for building trees may not result in good predictive distributions and propose changing the splitting criteria for trees to one based on proper scoring rules. Analysis of both simulated data and several real datasets demonstrates that using these new splitting criteria results in trees with improved predictive properties considering the entire predictive distribution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_11052 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Building Trees for Probabilistic Prediction via Scoring Rules Shashaani, Sara Surer, Ozge Plumlee, Matthew Guikema, Seth Methodology Machine Learning Decision trees built with data remain in widespread use for nonparametric prediction. Predicting probability distributions is preferred over point predictions when uncertainty plays a prominent role in analysis and decision-making. We study modifying a tree to produce nonparametric predictive distributions. We find the standard method for building trees may not result in good predictive distributions and propose changing the splitting criteria for trees to one based on proper scoring rules. Analysis of both simulated data and several real datasets demonstrates that using these new splitting criteria results in trees with improved predictive properties considering the entire predictive distribution. |
| title | Building Trees for Probabilistic Prediction via Scoring Rules |
| topic | Methodology Machine Learning |
| url | https://arxiv.org/abs/2402.11052 |