This study evaluates the performance of various classifiers in three distinct models: response, risk, and response-risk, concerning credit card mail campaigns and default prediction. In the response model, the Extra Trees classifier demonstrates the highest recall level (79.1%), emphasizing its effectiveness in identifying potential responders to targeted credit card offers. Conversely, in the risk model, the Random Forest classifier exhibits remarkable specificity of 84.1%, crucial for identifying customers least likely to default. Furthermore, in the multi-class response-risk model, the Random Forest classifier achieves the highest accuracy (83.2%), indicating its efficacy in discerning both potential responders to credit card mail campaign and low-risk credit card users. In this study, we optimized various performance metrics to solve a specific credit risk and mail responsiveness business problem.

翻译：本研究评估了三种不同模型（响应模型、风险模型及响应-风险模型）中多种分类器在信用卡邮件营销与违约预测方面的性能表现。在响应模型中，Extra Trees分类器展现出最高的召回率（79.1%），突显其在识别目标信用卡产品潜在响应者方面的有效性。相反，在风险模型中，随机森林分类器表现出84.1%的卓越特异度，这对于识别最不可能违约的客户至关重要。此外，在多类别响应-风险模型中，随机森林分类器取得了最高准确率（83.2%），表明其能有效区分信用卡邮件营销的潜在响应者与低风险信用卡用户。本研究通过优化多项性能指标，解决了特定的信用风险与邮件响应度商业问题。