Asymptotic properties of high-dimensional random forests
研究了原始随机森林算法（使用CART分裂准则）在高维非参数回归中的一致性速率，通过偏差-方差分解证明其能适应高维并允许回归函数不连续，并明确了偏差如何依赖于样本量、树高和列子采样参数。
As a flexible nonparametric learning tool, the random forests algorithm has been widely applied to various real applications with appealing empirical performance, even in the presence of high-dimensional feature space. Unveiling the underlying mechanisms has led to some important recent theoretical results on the consistency of the random forests algorithm and its variants. However, to our knowledge, almost all existing works concerning random forests consistency in a high-dimensional setting were established for various modified random forests models where the splitting rules are independent of the response; a few exceptions assume simple data generating models with binary features. In light of this, in this paper we derive the consistency rates for the random forests algorithm associated with the sample CART splitting criterion, which is the one used in the original version of the algorithm (Mach. Learn. 45 (2001) 5–32), in a general high-dimensional nonparametric regression setting through a bias-variance decomposition analysis. Our new theoretical results show that random forests can indeed adapt to high dimensionality and allow for discontinuous regression function. Our bias analysis characterizes explicitly how the random forests bias depends on the sample size, tree height and column subsampling parameter. Some limitations on our current results are also discussed.