We make three contributions in this paper. First, we present an approach for compiling a random forest classifier into a set of circuits, where each circuit directly encodes the instances in some class of the classifier. We show empirically that our proposed approach is significantly more efficient than existing similar approaches. Next, we utilize this approach to further obtain circuits that are tractable for computing the complete and general reasons of a decision, which are instance abstractions that play a fundamental role in computing explanations. Finally, we propose algorithms for computing the robustness of a decision and all shortest ways to flip it. We illustrate the utility of our contributions by using them to enumerate all sufficient reasons, necessary reasons and contrastive explanations of decisions; to compute the robustness of decisions; and to identify all shortest ways to flip the decisions made by random forest classifiers learned from a wide range of datasets.

翻译：本文提出三点贡献。首先，我们提出一种将随机森林分类器编译为电路集合的方法，其中每个电路直接编码分类器某个类别中的实例。实验表明，我们提出的方法比现有类似方法显著更高效。其次，利用该方法进一步获得可处理的电路，用于计算决策的完整与通用原因——这些实例抽象在计算解释中起着基础性作用。最后，我们提出计算决策鲁棒性及所有最短决策翻转路径的算法。通过将其应用于枚举决策的所有充分原因、必要原因与对比解释，计算决策鲁棒性，以及识别从广泛数据集中学习的随机森林分类器决策的所有最短翻转路径，我们展示了这些贡献的实用价值。