Hand David J, Christen Peter, Kirielle Nishadi
Imperial College London, London, UK.
School of Computer Science, The Australian National University, Canberra, Australia.
Mach Learn. 2021;110(3):451-456. doi: 10.1007/s10994-021-05964-1. Epub 2021 Mar 15.
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-star), which has an immediate practical interpretation.
F 度量，也称为 F1 分数，被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，质疑将精度和召回率这两个在概念上截然不同的性能方面进行组合的适当性，也质疑调和平均数是否是组合它们的最佳方式。为了缓解这种担忧，我们描述了一种对 F 度量的简单变换，我们称之为（F 星），它具有直接的实际解释。
