选对评估指标，让分类结果更符合实际需求。
Selecting a classification performance measure: matching the measure to the problem
- 根据任务目标匹配评估指标，避免误导性结论。
- 不同指标在错误类型敏感度上差异显著。
- 适用于医疗、金融等高风险决策场景的评估指导。
识别对象属于给定类别中的哪一个，是众多研究领域和应用中的普遍问题，涵盖医学诊断、金融决策、在线商业和国家安全等。但分类结果很少完全准确，分类错误不可避免。因此必须比较不同分类方法和算法，以确定哪个最适合特定问题。然而，分类方法多样，性能衡量方式也多种多样。选择与研究或应用目标相匹配的性能度量至关重要。本文是对现有性能度量优劣比较文献的重要补充，重点强调度量属性与分类目的匹配的关键性。
原文摘要 · Abstract (English)
The problem of identifying to which of a given set of classes objects belong is ubiquitous, occurring in many research domains and application areas, including medical diagnosis, financial decision making, online commerce, and national security. But such assignments are rarely completely perfect, and classification errors occur. This means it is necessary to compare classification methods and algorithms to decide which is ``best'' for any particular problem. However, just as there are many different classification methods, so there are many different ways of measuring their performance. It is thus vital to choose a measure of performance which matches the aims of the research or application. This paper is a contribution to the growing literature on the relative merits of different performance measures. Its particular focus is the critical importance of matching the properties of the measure to the aims for which the classification is being made.
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