多类分类器研究综述
Metrics for Multi-Class Classification: an Overview
摘要：
机器学习中涉及两个以上类的分类任务被称为“多类分类”。当目标是评估和比较不同的分类模型或机器学习技术时，性能指标非常有用。许多指标都可以用来测试多类分类器的能力。这些指标在开发过程的不同阶段都很有用，例如，比较两个不同模型的性能或通过调整不同参数来分析同一模型的行为。在本白皮书中，我们回顾了一系列最有前途的多类指标，强调了它们的优点和缺点，并展示了它们在分类模型开发过程中的可能用途。
英文摘要：
Classification tasks in machine learning involving more than two classes are known by the name of "multi-class classification". Performance indicators are very useful when the aim is to evaluate and compare different classification models or machine learning techniques. Many metrics come in handy to test the ability of a multi-class classifier. Those metrics turn out to be useful at different stage of the development process, e.g. comparing the performance of two different models or analysing the behaviour of the same model by tuning different parameters. In this white paper we review a list of the most promising multi-class metrics, we highlight their advantages and disadvantages and show their possible usages during the development of a classification model.
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。
