# Grafting: Making Random Forests Consistent

arXiv: https://arxiv.org/abs/2403.06015 · HTML: https://mlsanity.com/paper/2403.06015/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 32 references that are in the map.
- First posted on arXiv 9 Mar 2024.

## Builds on (most cited first, 7 of 7)

- [Recursive Partitioning for Heterogeneous Causal Effects](https://mlsanity.com/paper/1504.01132/) (2015)
- [A Random Forest Guided Tour](https://mlsanity.com/paper/1511.05741/) (2015)
- [Consistency of random forests](https://mlsanity.com/paper/1405.2881/) (2014)
- [Iterative Random Forests to detect predictive and stable high-order interactions](https://mlsanity.com/paper/1706.08457/) (2017)
- [Minimax optimal rates for Mondrian trees and forests](https://mlsanity.com/paper/1803.05784/) (2018)
- [Sparse learning with CART](https://mlsanity.com/paper/2006.04266/) (2020)
- [Predicting United States policy outcomes with Random Forests](https://mlsanity.com/paper/2008.07338/) (2020)

## Abstract (arXiv)

Despite their performance and widespread use, little is known about the theory of Random Forests. A major unanswered question is whether, or when, the Random Forest algorithm is consistent. The literature explores various variants of the classic Random Forest algorithm to address this question and known short-comings of the method. This paper is a contribution to this literature. Specifically, the suitability of grafting consistent estimators onto a shallow CART is explored. It is shown that this approach has a consistency guarantee and performs well in empirical settings.