Interpretable machine learning for imbalanced credit scoring datasets
Interpretable machine learning for imbalanced credit scoring datasets
复制标题
用于不平衡信用评分数据集的可解释机器学习
DOI：
10.1016/j.ejor.2023.06.036
复制
发表时间：
2023-08-18
影响因子：
6.4
通讯作者：
Martin-Barragan, Belen
中科院分区：
文献类型：
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作者：
Chen, Yujia;Calabrese, Raffaella;Martin-Barragan, Belen
类别不平衡问题在信用评分领域很常见，因为违约者的数量通常远少于非违约者的数量。迄今为止，关于类别不平衡问题的研究主要集中在指示和减少类别不平衡对机器学习技术的预测准确性的不利影响，而文献中从未研究过类别不平衡对机器学习可解释性的影响。本文通过分析局部可解释模型不可知解释（LIME）和 SHapley 加法解释（SHAP）这两种流行的解释方法的稳定性如何受到类不平衡的影响，填补了这一空白。我们的实验使用从欧洲数据仓库收集的 2016-2020 年英国住宅抵押贷款数据。我们评估了 LIME 和 SHAP 在类别不平衡逐渐增加的数据集上的稳定性。结果表明，随着类别不平衡的增加，LIME 和 SHAP 生成的解释不太稳定，这表明类别不平衡确实对机器学习的可解释性产生不利影响。为了检查我们结果的稳健性，我们还分析了两个开源信用评分数据集，并获得了类似的结果。 2023 作者。由 Elsevier B.V 出版。这是一篇基于 CC BY 许可证的开放获取文章 (http://creativecommons.org/licenses/by/4.0/)
The class imbalance problem is common in the credit scoring domain, as the number of defaulters is usually much less than the number of non-defaulters. To date, research on investigating the class imbalance problem has mainly focused on indicating and reducing the adverse effect of the class imbalance on the predictive accuracy of machine learning techniques, while the impact of that on machine learning interpretability has never been studied in the literature. This paper fills this gap by analysing how the stability of Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP), two popular interpretation methods, are affected by class imbalance. Our experiments use 2016-2020 UK residential mortgage data collected from European Datawarehouse. We evaluate the stability of LIME and SHAP on datasets of progressively increased class imbalance. The results show that interpretations generated from LIME and SHAP are less stable as the class imbalance increases, which indicates that the class imbalance does have an adverse effect on machine learning interpretability. To check the robustness of our outcomes, we also analyse two open-source credit scoring datasets and we obtain similar results.& COPY; 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )