# Ensembles of Probabilistic Regression Trees

arXiv: https://arxiv.org/abs/2406.14033 · HTML: https://mlsanity.com/paper/2406.14033/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 22 references that are in the map.
- First posted on arXiv 20 Jun 2024.

## Builds on (most cited first, 2 of 2)

- [Consistency of random forests](https://mlsanity.com/paper/1405.2881/) (2014)
- [Bayesian Regression Tree Ensembles that Adapt to Smoothness and Sparsity](https://mlsanity.com/paper/1707.09461/) (2017)

## Built on by (most cited first, 1 of 1)

- [Anchor-MoE: A Mean-Anchored Mixture of Experts For Probabilistic Regression](https://mlsanity.com/paper/2508.16802/) (2025)

## Abstract (arXiv)

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.