Random Forests (Breiman, 2001) is a successful and widely used regression and classification algorithm. Part of its appeal and reason for its versatility is its (implicit) construction of a kernel-type weighting function on training data, which can also be used for targets other than the original mean estimation. We propose a novel forest construction for multivariate responses based on their joint conditional distribution, independent of the estimation target and the data model. It uses a new splitting criterion based on the MMD distributional metric, which is suitable for detecting heterogeneity in multivariate distributions. The induced weights define an estimate of the full conditional distribution, which in turn can be used for arbitrary and potentially complicated targets of interest. The method is very versatile and convenient to use, as we illustrate on a wide range of examples. The code is available as Python and R packages drf.

翻译：随机森林(Breiman, 2001)是一个成功和广泛使用的回归和分类算法,其部分吸引力和多功能性的部分原因在于它(隐含)在培训数据上构建一个内核型加权功能,该功能也可以用于原始平均估计以外的目标。我们提议在不依赖估计目标和数据模型的情况下,以联合有条件分布为基础,为多变量反应构筑新的森林结构。它使用基于MMD分布度的新分解标准,适合检测多变量分布中的异质。引力加权确定了完全有条件分布的估计数,而后者又可用于任意和潜在的复杂利益目标。该方法非常灵活和方便地使用,我们举例说明了广泛的例子。代码作为Python和R软件包Drf提供。