Beyond Point Estimates: Reliable Evaluation of Prediction Performance Metrics under Clustered Data
超越点估计：聚类数据下预测性能指标的可靠评估AI 解读 ↓
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超越点估计：聚类数据下预测性能指标的可靠评估
- 研究问题
- 当预测评估数据存在重复测量或聚类依赖时，如何为准确率、F1 等指标提供可靠的不确定性度量，并支持达标决策和研究设计？
- 方法与思路
- 作者将多类性能指标表示为混淆矩阵概率的光滑泛函，结合聚类稳健三明治方差，构造区间、检验、配对比较及样本量近似。
- 材料陈述的贡献
- 摘要称，该框架统一二分类与多分类评估，模拟中覆盖率接近名义水平，并展示忽略聚类可能低估变异、改变应用结论。
- 阅读关注点
- 应核对聚类间依赖和聚类数量的要求、指标分母接近零时的处理，以及配对比较、功效与样本量近似的适用条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收均未核实；摘要未列出渐近条件及模拟数值，不能保证任意依赖结构下均有效。
原文摘要
Prediction performance metrics such as accuracy and the F1 score are typically reported as single numbers, with no measure of uncertainty. The omission has been tolerable in exploratory settings, where model evaluation is used for informal comparison rather than formal decision-making. But as machine learning is deployed in real-world applications, evaluation results are increasingly used to support binary decisions -- whether a model meets a required standard or not -- making uncertainty quantification essential. The problem is compounded when data are dependent, as in repeated measurements, clustered subjects, or time series, where variability is harder to assess and easy to underestimate. We develop a unified framework that links a broad class of performance metrics through their representation as smooth functionals of confusion-matrix probabilities. This representation allows the use of the cluster-robust sandwich variance estimator to obtain asymptotically valid confidence intervals, hypothesis tests, and paired model comparisons for both binary and multiclass problems under clustered data. We also provide power and sample size approximations based on pilot data, enabling principled study design for model evaluation. Simulations show that the proposed methods achieve near-nominal coverage across a range of dependence structures, while naive methods underestimate variability. A real-data application further illustrates how accounting for clustering can materially change conclusions. These results offer a practical foundation for uncertainty quantification and study design in prediction performance evaluation, in settings where decisions should be justified under dependent and clustered data.