The F-measure, also known as the F1-score, is widely used to assess the performance of classification algorithms. However, some researchers find it lacking in intuitive interpretation, questioning the appropriateness of combining two aspects of performance as conceptually distinct as precision and recall, and also questioning whether the harmonic mean is the best way to combine them. To ease this concern, we describe a simple transformation of the F-measure, which we call \(F^*\) (F-star), which has an immediate practical interpretation.

中文翻译：

---

F*：F 度量的可解释转换

F-measure，也称为 F1-score，广泛用于评估分类算法的性能。然而，一些研究人员发现它缺乏直观的解释，质疑将性能的两个方面结合起来的适当性，如精确度和召回率，以及调和平均值是否是结合它们的最佳方式。为了缓解这种担忧，我们描述了 F-measure 的一个简单转换，我们称之为\(F^*\) (F-star)，它具有直接的实际解释。