Several performance measures can be used for evaluating classification
results: accuracy, F-measure, and many others. Can we say that some of them are
better than others, or, ideally, choose one measure that is best in all
situations? To answer this question, we conduct a systematic analysis of
classification performance measures: we formally define a list of desirable
properties and theoretically analyze which measures satisfy which properties.
We also prove an impossibility theorem: some desirable properties cannot be
simultaneously satisfied. Finally, we propose a new family of measures
satisfying all desirable properties except one. This family includes the
Matthews Correlation Coefficient and a so-called Symmetric Balanced Accuracy
that was not previously used in classification literature. We believe that our
systematic approach gives an important tool to practitioners for adequately
evaluating classification results.

通过定义和分析分类表现的可取特性，研究论证了某些性能度量方法比其他方法更好，并提出了一系列新的度量方法，这些方法在除一种可取特性外均可满足，包括 Matthews 相关系数和对称平衡准确性，为实践人员实现充分的分类结果评估提供了重要工具。