为数据模型验证提供可落地的通用规则，提升结果透明度与可信度。
A Set of Rules for Model Validation
- 提出一套通用验证规则，指导实践者设计可靠验证方案。
- 强调报告性能指标时需清晰可比，避免误导性结论。
- 适合科研与工业界建模人员参考，提升模型评估规范性。
数据驱动模型的验证是评估其在目标总体中对新、未见数据泛化能力的过程。本文提出一套通用的模型验证规则，旨在帮助实践者制定可靠的验证计划，并透明地报告结果。尽管任何验证方案都不完美，但这些规则能确保策略足以满足实际应用需求，公开讨论验证方法的局限性，并报告清晰、可比较的性能指标。
原文摘要 · Abstract (English)
The validation of a data-driven model is the process of assessing the model's ability to generalize to new, unseen data in the population of interest. This paper proposes a set of general rules for model validation. These rules are designed to help practitioners create reliable validation plans and report their results transparently. While no validation scheme is flawless, these rules can help practitioners ensure their strategy is sufficient for practical use, openly discuss any limitations of their validation strategy, and report clear, comparable performance metrics.
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