📄 中文摘要
在信用评分模型的构建过程中，处理借款人数据中的异常值和缺失值是至关重要的。通过使用Python，提供了一系列方法来识别和处理数据中的异常值，包括使用统计方法和可视化工具。此外，缺失值的处理方法也被详细介绍，包括删除缺失值、插补和使用模型预测等策略。这些技术的应用能够有效提高信用评分模型的准确性和可靠性，从而为金融机构提供更为科学的决策依据。
构建稳健的信用评分模型（第三部分）
出处: Building Robust Credit Scoring Models (Part 3)
发布: 2026年3月20日
📄 English Summary
Building Robust Credit Scoring Models (Part 3)
Handling outliers and missing values in borrower data is crucial for building robust credit scoring models. A range of methods are provided using Python to identify and manage outliers, including statistical techniques and visualization tools. Additionally, various strategies for addressing missing values are discussed, such as deletion, imputation, and model-based predictions. The application of these techniques enhances the accuracy and reliability of credit scoring models, offering financial institutions a more scientific basis for decision-making.