# Large Random Forests: Optimisation for Rapid Evaluation

arXiv: https://arxiv.org/abs/1912.10934 · HTML: https://mlsanity.com/paper/1912.10934/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Dec 2019.

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

- [Optimal Decision Diagrams for Classification](https://mlsanity.com/paper/2205.14500/) (2022)

## Abstract (arXiv)

Random Forests are one of the most popular classifiers in machine learning. The larger they are, the more precise is the outcome of their predictions. However, this comes at a cost: their running time for classification grows linearly with the number of trees, i.e. the size of the forest. In this paper, we propose a method to aggregate large Random Forests into a single, semantically equivalent decision diagram. Our experiments on various popular datasets show speed-ups of several orders of magnitude, while, at the same time, also significantly reducing the size of the required data structure.