We consider finding a counterfactual explanation for a classification or
regression forest, such as a random forest. This requires solving an
optimization problem to find the closest input instance to a given instance for
which the forest outputs a desired value. Finding an exact solution has a cost
that is exponential on the number of leaves in the forest. We propose a simple
but very effective approach: we constrain the optimization to only those input
space regions defined by the forest that are populated by actual data points.
The problem reduces to a form of nearest-neighbor search using a certain
distance on a certain dataset. This has two advantages: first, the solution can
be found very quickly, scaling to large forests and high-dimensional data, and
enabling interactive use. Second, the solution found is more likely to be
realistic in that it is guided towards high-density areas of input space.

本文提出一种简单且有效的方法，将寻找分类或回归森林的反事实解释所需的最优化问题限制在由实际数据点填充的森林定义的输入空间区域，并使用某个数据集上的某个距离的最近邻搜索来解决问题。这种解决方式有两个优点：能够快速找到解决方案，并且更有可能是现实的，因为它是引导向输入空间的高密度区域的。