# Bayesian Decision Trees Inspired from Evolutionary Algorithms

arXiv: https://arxiv.org/abs/2305.18774 · HTML: https://mlsanity.com/paper/2305.18774/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 27 references that are in the map.
- First posted on arXiv 30 May 2023.

## Builds on (most cited first, 4 of 4)

- [On Theory for BART](https://mlsanity.com/paper/1810.00787/) (2018)
- [Parallel Markov Chain Monte Carlo via Spectral Clustering](https://mlsanity.com/paper/1605.09454/) (2016)
- [Single MCMC Chain Parallelisation on Decision Trees](https://mlsanity.com/paper/2207.12688/) (2022)
- [Parallel Approaches to Accelerate Bayesian Decision Trees](https://mlsanity.com/paper/2301.09090/) (2023)

## Abstract (arXiv)

Bayesian Decision Trees (DTs) are generally considered a more advanced and accurate model than a regular Decision Tree (DT) because they can handle complex and uncertain data. Existing work on Bayesian DTs uses Markov Chain Monte Carlo (MCMC) with an accept-reject mechanism and sample using naive proposals to proceed to the next iteration, which can be slow because of the burn-in time needed. We can reduce the burn-in period by proposing a more sophisticated way of sampling or by designing a different numerical Bayesian approach. In this paper, we propose a replacement of the MCMC with an inherently parallel algorithm, the Sequential Monte Carlo (SMC), and a more effective sampling strategy inspired by the Evolutionary Algorithms (EA). Experiments show that SMC combined with the EA can produce more accurate results compared to MCMC in 100 times fewer iterations.