论文标题
多级分类的指标：概述
Metrics for Multi-Class Classification: an Overview
论文作者
论文摘要
涉及两个以上类的机器学习中的分类任务以“多类分类”的名称知道。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。许多指标派上用场来测试多级分类器的能力。这些指标在开发过程的不同阶段很有用，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。在这份白皮书中，我们回顾了最有希望的多级指标列表，我们强调了它们的优势和缺点，并在开发分类模型期间显示了它们可能的用法。
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.
