Ensembles of Probabilistic Regression Trees
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910495469993984 |
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| author | Seiller, Alexandre Gaussier, Éric Devijver, Emilie Clausel, Marianne Alkhoury, Sami |
| author_facet | Seiller, Alexandre Gaussier, Éric Devijver, Emilie Clausel, Marianne Alkhoury, Sami |
| contents | Tree-based ensemble methods such as random forests, gradient-boosted trees, and Bayesianadditive regression trees have been successfully used for regression problems in many applicationsand research studies. In this paper, we study ensemble versions of probabilisticregression trees that provide smooth approximations of the objective function by assigningeach observation to each region with respect to a probability distribution. We prove thatthe ensemble versions of probabilistic regression trees considered are consistent, and experimentallystudy their bias-variance trade-off and compare them with the state-of-the-art interms of performance prediction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_14033 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Ensembles of Probabilistic Regression Trees Seiller, Alexandre Gaussier, Éric Devijver, Emilie Clausel, Marianne Alkhoury, Sami Machine Learning Tree-based ensemble methods such as random forests, gradient-boosted trees, and Bayesianadditive regression trees have been successfully used for regression problems in many applicationsand research studies. In this paper, we study ensemble versions of probabilisticregression trees that provide smooth approximations of the objective function by assigningeach observation to each region with respect to a probability distribution. We prove thatthe ensemble versions of probabilistic regression trees considered are consistent, and experimentallystudy their bias-variance trade-off and compare them with the state-of-the-art interms of performance prediction. |
| title | Ensembles of Probabilistic Regression Trees |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2406.14033 |