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.

翻译：尽管随机森林性能优异且被广泛使用，但其理论基础仍知之甚少。一个尚未解决的核心问题是随机森林算法是否具有一致性（或何时具有一致性）。现有文献通过探索经典随机森林算法的多种变体来回应这一疑问，并弥补该方法已知的局限性。本文正是对此类文献的贡献：具体而言，我们探究了将一致估计量嫁接到浅层CART上的可行性。研究表明，该方法不仅具备一致性保证，在实证场景中亦表现优异。