The ability to explain why a machine learning model arrives at a particular prediction is crucial when used as decision support by human operators of critical systems. The provided explanations must be provably correct, and preferably without redundant information, called minimal explanations. In this paper, we aim at finding explanations for predictions made by tree ensembles that are not only minimal, but also minimum with respect to a cost function. To this end, we first present a highly efficient oracle that can determine the correctness of explanations, surpassing the runtime performance of current state-of-the-art alternatives by several orders of magnitude when computing minimal explanations. Secondly, we adapt an algorithm called MARCO from related works (calling it m-MARCO) for the purpose of computing a single minimum explanation per prediction, and demonstrate an overall speedup factor of two compared to the MARCO algorithm which enumerates all minimal explanations. Finally, we study the obtained explanations from a range of use cases, leading to further insights of their characteristics. In particular, we observe that in several cases, there are more than 100,000 minimal explanations to choose from for a single prediction. In these cases, we see that only a small portion of the minimal explanations are also minimum, and that the minimum explanations are significantly less verbose, hence motivating the aim of this work.
翻译:解释机器学习模型为何得出特定预测的能力对于关键系统的人类操作者作为决策支持而使用关键系统至关重要。所提供的解释必须准确,最好没有多余的信息,称为最低限度的解释。在本文中,我们的目标是为树群所作的预测找到解释,这些预测不仅最低,而且对成本功能也最低。为此目的,我们首先提出一个高效的神器,可以确定解释的正确性,超过当前最先进替代品的运行时间性能,在计算最低解释时以几个数量级的速度计算。第二,我们从相关作品(调用MARCO)改用称为MARCO的算法,以计算每个预测的单一最低解释,并显示与计算所有最低解释的MARCO算法相比,总加速系数为2,最后,我们研究从一系列使用案例获得的解释,从而进一步了解其特性。我们特别注意到,在一些情况下,在计算最低解释时,从单一预测中可选择10万以上最低限度的解释。在这些案例中,我们发现,只有很小一部分解释,因此,最低解释的目的也是最小的。</s>