ROC曲线与精确率-召回率曲线的空间聚合
Spatial Aggregation of ROC and Precision-Recall Curves
浏览论文内容
中文总结 AI 辅助
本研究探讨ROC和PR曲线在空间预报中的不同聚合策略对判别能力评估的影响，提出满足优势保持与凹性保持的两种新策略，并通过AI全球天气预报实例展示聚合策略可改变预报排名，为曲线聚合解释提供实用指南。
中文摘要 AI 辅助
接收者操作特征（ROC）曲线和精确率-召回率（PR）曲线被广泛用于评估二元事件（如阈值超越或极端事件预警）预报的判别能力。在天气预报中，预报以空间场的形式提供，从而产生逐位置的ROC和PR曲线，这些曲线通常被聚合以方便比较。然而，聚合策略对性能评估的影响仍知之甚少。我们研究了ROC和PR曲线的不同聚合策略如何影响判别能力的评估。具体而言，我们确定了聚合策略满足公平比较的两个理想性质的条件：预报之间优势关系的保持以及曲线凹性或可达性的保持。我们获得了充分条件，并提出了满足这些条件的两种策略。它们与文献中已有的策略进行了比较，我们分析了它们的性质，并强调了可能导致误导性解释的潜在陷阱。基于这些发现，我们为聚合ROC和PR曲线的解释提供了实用指南。所提出的框架通过基于人工智能的全球天气预报进行了说明，展示了不同的聚合策略如何导致竞争预报的不同排名。
英文摘要
Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves are widely used to assess the discrimination ability of forecasts for binary events, such as threshold exceedances or warnings of extreme events. In weather forecasting, forecasts are provided as spatial fields, yielding location-wise ROC and PR curves that are often aggregated to facilitate comparison. However, the effect of the aggregation strategy on performance assessment remains poorly understood. We investigate how different aggregation strategies for ROC and PR curves affect the assessment of discrimination ability. In particular, we identify conditions under which aggregation strategies satisfy two desirable properties for fair comparison: preservation of dominance between forecasts and preservation of concavity or achievability of the curves. We obtain sufficient conditions and propose two strategies satisfying them. They are compared with existing strategies from the literature, and we analyze their properties and highlight potential pitfalls that may lead to misleading interpretations. Based on these findings, we provide practical guidelines for the interpretation of aggregated ROC and PR curves. The proposed framework is illustrated with AI-based global weather forecasts, showing how different aggregation strategies can yield different rankings of competing forecasts.
发表机构
- ETH Zurich（苏黎世联邦理工学院）
- University of Geneva（日内瓦大学）
- Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
机构由 AI 辅助整理，请以论文原文为准。
