Ensembles of Probabilistic Regression Trees

Alexandre Seiller; Éric Gaussier; Emilie Devijver; Marianne Clausel; Sami Alkhoury

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arXiv · 2406.14033

Ensembles of Probabilistic Regression Trees

Alexandre Seiller·Éric Gaussier·Emilie Devijver·Marianne Clausel·Sami Alkhoury

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Abstract

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.

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Alexandre Seiller, Éric Gaussier, Emilie Devijver, Marianne Clausel, Sami Alkhoury. 2024-06-20. Ensembles of Probabilistic Regression Trees. https://arxiv.org/abs/2406.14033

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