The tree ensemble model has been widely employed as a loan evaluation method in credit risk assessment due to its high accuracy and robustness. However, the tree ensemble model is complex and incomprehensible, which restricts its adoption for decision-making in loan evaluation. In this paper, we propose a novel rule extraction method for improving the ensemble model by balancing predictive performance and interpretability in two stages: a local rule extraction method followed by a global rule extraction method. The local method simplifies each rule by removing its redundant constraints, while the global method optimizes the complete rule set based on the multiobjective optimization method. An interpretable rule-based model is extracted from the tree ensemble model via the proposed method. Comparing the performance to six other methods on three loan evaluation datasets, the proposed method shows superior interpretability and realizes similar predictive performance to the tree ensemble model. For practical loan evaluation, the proposed method provides decision-makers with an interpretable rule-based model, which could replace the opaque tree ensemble model in high-stakes decision-making. In addition, the proposed method could facilitate decision-makers in explaining the tree ensemble model by analyzing the important and valuable rules that are extracted from the original opaque model.

树集成模型由于其高精度和鲁棒性而被广泛用作信用风险评估中的贷款评估方法。然而，树集成模型复杂且难以理解，限制了其在贷款评估决策中的采用。在本文中，我们提出了一种新的规则提取方法，通过平衡两个阶段的预测性能和可解释性来改进集成模型：局部规则提取方法和全局规则提取方法。局部方法通过去除其冗余约束来简化每个规则，而全局方法基于多目标优化方法优化完整规则集。通过所提出的方法从树集成模型中提取可解释的基于规则的模型。在三个贷款评估数据集上将性能与其他六种方法进行比较，所提出的方法显示出卓越的可解释性，并实现了与树集成模型相似的预测性能。对于实际贷款评估，所提出的方法为决策者提供了一个可解释的基于规则的模型，可以在高风险决策中取代不透明的树集成模型。此外，所提出的方法可以通过分析从原始不透明模型中提取的重要和有价值的规则来帮助决策者解释树集成模型。所提出的方法为决策者提供了一个可解释的基于规则的模型，可以在高风险决策中取代不透明的树集成模型。此外，所提出的方法可以通过分析从原始不透明模型中提取的重要和有价值的规则来帮助决策者解释树集成模型。所提出的方法为决策者提供了一个可解释的基于规则的模型，可以在高风险决策中取代不透明的树集成模型。此外，所提出的方法可以通过分析从原始不透明模型中提取的重要和有价值的规则来帮助决策者解释树集成模型。