We present FIRE, Fast Interpretable Rule Extraction, an optimization-based framework to extract a small but useful collection of decision rules from tree ensembles. FIRE selects sparse representative subsets of rules from tree ensembles, that are easy for a practitioner to examine. To further enhance the interpretability of the extracted model, FIRE encourages fusing rules during selection, so that many of the selected decision rules share common antecedents. The optimization framework utilizes a fusion regularization penalty to accomplish this, along with a non-convex sparsity-inducing penalty to aggressively select rules. Optimization problems in FIRE pose a challenge to off-the-shelf solvers due to problem scale and the non-convexity of the penalties. To address this, making use of problem-structure, we develop a specialized solver based on block coordinate descent principles; our solver performs up to 40x faster than existing solvers. We show in our experiments that FIRE outperforms state-of-the-art rule ensemble algorithms at building sparse rule sets, and can deliver more interpretable models compared to existing methods.

翻译：我们提出FIRE（Fast Interpretable Rule Extraction，快速可解释规则提取），这是一种基于优化的框架，用于从树集成模型中提取小而有效的决策规则集合。FIRE从树集成中选择稀疏的代表性子集规则，便于实践者检查。为进一步增强提取模型的可解释性，FIRE在规则选择过程中鼓励规则融合，使得许多选中的决策规则共享共同的前件。该优化框架利用融合正则化惩罚项实现这一目标，并结合非凸稀疏诱导惩罚项以积极选择规则。由于问题规模及惩罚项的非凸性，FIRE中的优化问题对现成求解器构成挑战。为解决这一问题，我们利用问题结构，基于块坐标下降原理开发专用求解器；该求解器的运行速度比现有求解器快达40倍。实验表明，FIRE在构建稀疏规则集方面优于最先进的规则集成算法，并能比现有方法提供更具可解释性的模型。