# Data-driven multinomial random forest

arXiv: https://arxiv.org/abs/2304.04240 · HTML: https://mlsanity.com/paper/2304.04240/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 22 references that are in the map.
- First posted on arXiv 9 Apr 2023.

## Builds on (most cited first, 3 of 3)

- [Neural Random Forests](https://mlsanity.com/paper/1604.07143/) (2016)
- [Narrowing the Gap: Random Forests In Theory and In Practice](https://mlsanity.com/paper/1310.1415/) (2013)
- [Rectified Decision Trees: Exploring the Landscape of Interpretable and Effective Machine Learning](https://mlsanity.com/paper/2008.09413/) (2020)

## Abstract (arXiv)

In this article, we strengthen the proof methods of some previously weakly consistent variants of random forests into strongly consistent proof methods, and improve the data utilization of these variants, in order to obtain better theoretical properties and experimental performance. In addition, based on the multinomial random forest (MRF) and Bernoulli random forest (BRF), we propose a data-driven multinomial random forest (DMRF) algorithm, which has lower complexity than MRF and higher complexity than BRF while satisfying strong consistency. It has better performance in classification and regression problems than previous RF variants that only satisfy weak consistency, and in most cases even surpasses standard random forest. To the best of our knowledge, DMRF is currently the most excellent strongly consistent RF variant with low algorithm complexity