Random Forest (RF) is well-known as an efficient ensemble learning method in terms of predictive performance. It is also considered a Black Box because of its hundreds of deep decision trees. This lack of interpretability can be a real drawback for acceptance of RF models in several real-world applications, especially those affecting one's lives, such as in healthcare, security, and law. In this work, we present Forest-ORE, a method that makes RF interpretable via an optimized rule ensemble (ORE) for local and global interpretation. Unlike other rule-based approaches aiming at interpreting the RF model, this method simultaneously considers several parameters that influence the choice of an interpretable rule ensemble. Existing methods often prioritize predictive performance over interpretability coverage and do not provide information about existing overlaps or interactions between rules. Forest-ORE uses a mixed-integer optimization program to build an ORE that considers the trade-off between predictive performance, interpretability coverage, and model size (size of the rule ensemble, rule lengths, and rule overlaps). In addition to providing an ORE competitive in predictive performance with RF, this method enriches the ORE through other rules that afford complementary information. It also enables monitoring of the rule selection process and delivers various metrics that can be used to generate a graphical representation of the final model. This framework is illustrated through an example, and its robustness is assessed through 36 benchmark datasets. A comparative analysis of well-known methods shows that Forest-ORE provides an excellent trade-off between predictive performance, interpretability coverage, and model size.

翻译：摘要：随机森林作为一种集成学习方法，以其预测性能高效而著称。然而，由于其包含数百棵深层决策树，它也被视为一个黑箱模型。这种可解释性的缺失，在现实应用中（尤其是影响人们生活的领域，如医疗、安全与法律）可能成为阻碍RF模型被接受的实际障碍。本文提出Forest-ORE方法，通过优化规则集成实现RF的局部与全局可解释性。与基于规则的其他RF模型解释方法不同，本方法同时考虑了影响可解释规则集成选择的多个参数。现有方法往往优先考虑预测性能而非可解释覆盖度，且未提供规则间重叠或交互的详细信息。Forest-ORE采用混合整数优化程序构建ORE，综合考虑预测性能、可解释覆盖度与模型规模（规则集成大小、规则长度及规则重叠度）之间的权衡。除提供与RF预测性能相当的ORE外，该方法还通过补充信息的其他规则丰富ORE，支持监控规则选择过程，并提供可用于生成最终模型图形化表示的多项指标。本文通过示例阐释该框架，并基于36个基准数据集评估其稳健性。对已知方法的比较分析表明，Forest-ORE在预测性能、可解释覆盖度与模型规模之间实现了优异的平衡。