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.

通过优化的规则集合 (ORE) 使随机森林 (RF) 变得可解释，通过权衡预测性能、可解释性覆盖率和模型大小的权衡，提供了优秀的预测性能、可解释性覆盖率和模型大小的平衡。

Forest-ORE：挖掘最佳规则集合解释随机森林模型

Forest-ORE: Mining Optimal Rule Ensemble to interpret Random Forest  models

This paper proposes a new framework for learning a rule ensemble model that
is both accurate and interpretable. A rule ensemble is an interpretable model
based on the linear combination of weighted rules. In practice, we often face
the trade-off between the accuracy and interpretability of rule ensembles. That
is, a rule ensemble needs to include a sufficiently large number of weighted
rules to maintain its accuracy, which harms its interpretability for human
users. To avoid this trade-off and learn an interpretable rule ensemble without
degrading accuracy, we introduce a new concept of interpretability, named local
interpretability, which is evaluated by the total number of rules necessary to
express individual predictions made by the model, rather than to express the
model itself. Then, we propose a regularizer that promotes local
interpretability and develop an efficient algorithm for learning a rule
ensemble with the proposed regularizer by coordinate descent with local search.
Experimental results demonstrated that our method learns rule ensembles that
can explain individual predictions with fewer rules than the existing methods,
including RuleFit, while maintaining comparable accuracy.

本文提出了一种新的框架来学习规则集合模型，该模型既准确又可解释，该模型的可解释性通过评估模型所需表达预测所需的规则数量来评估，并提出了一种促进局部可解释性的正则化器，通过局部搜索的坐标下降算法来学习规则集合。实验结果表明，与现有方法（包括 RuleFit）相比，我们的方法学习规则集合时所需规则数量更少，同时仍然保持相当的准确性。