# A Bayesian Decision Tree Algorithm

arXiv: https://arxiv.org/abs/1901.03214 · HTML: https://mlsanity.com/paper/1901.03214/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2019.

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

- [Dive into Decision Trees and Forests: A Theoretical Demonstration](https://mlsanity.com/paper/2101.08656/) (2021)

## Abstract (arXiv)

Bayesian Decision Trees are known for their probabilistic interpretability. However, their construction can sometimes be costly. In this article we present a general Bayesian Decision Tree algorithm applicable to both regression and classification problems. The algorithm does not apply Markov Chain Monte Carlo and does not require a pruning step. While it is possible to construct a weighted probability tree space we find that one particular tree, the greedy-modal tree (GMT), explains most of the information contained in the numerical examples. This approach seems to perform similarly to Random Forests.