在未见数据分布偏移下实现稳健的预测建模：一种方法论评论
Robust Predictive Modeling Under Unseen Data Distribution Shifts: A Methodological Commentary
AI总结：
本文通过实际客户流失案例，指出训练与测试数据分布不一致的问题，并探讨领域泛化方法，提出采用不确定性意识的预测建模方法以提升模型鲁棒性。
摘要：
大多数研究设计新型预测模型或使用现有模型时，假设训练和测试数据是独立同分布的。在实践中，服务时的数据往往偏离训练分布，导致性能下降和潜在的设计有效性或偏差测量问题。这一挑战进一步复杂化，因为服务时的数据在模型开发期间通常不可用。本文通过实际客户流失案例提高对此问题的认识，并回顾领域泛化文献，该领域是迁移学习的一个子领域，专门处理训练期间未见目标域的情况。我们进一步主张采用不确定性意识的预测建模方法，并通过分布鲁棒优化框架说明如何操作化这一视角。最后，我们提供了一些实用建议，以增强在未见数据分布偏移下的预测建模鲁棒性。
英文摘要：
Most research designing novel predictive models, or employing existing ones, assumes that training and testing data are independent and identically distributed. In practice, the data encountered at serving time often deviate from the training distribution, leading to substantial performance degradation and potential design validity and/or biased measurement issues. This challenge is further complicated by the fact that the serving time data are frequently unavailable during model development. This method commentary raises awareness of this overlooked issue through a real-world customer churn example and reviews the growing literature on domain generalization, a subfield of transfer learning that explicitly addresses situations in which the target domain is unseen during training. We further argue for adopting an uncertainty-aware predictive modeling mindset and illustrate how this perspective can be operationalized through the distributionally robust optimization framework. Finally, we offer several practical recommendations to enhance the robustness of predictive modeling under unseen data distribution shifts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
