Grafting: Making Random Forests Consistent
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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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 32 references that are in the map. First posted on arXiv 9 Mar 2024.
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7 of 32 references.
References · 7
- 2015Recursive Partitioning for Heterogeneous Causal Effectscited by 254
- 2015A Random Forest Guided Tourcited by 168
- 2014Consistency of random forestscited by 84
- 2017Iterative Random Forests to detect predictive and stable high-order interactionscited by 42
- 2018Minimax optimal rates for Mondrian trees and forestscited by 18
- 2020Sparse learning with CARTcited by 12
- 2020Predicting United States policy outcomes with Random Forestscited by 1
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