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F*: An Interpretable Transformation of the F-measure

Machine Learning 2021-03-19 v3 Artificial Intelligence Computer Vision and Pattern Recognition Information Retrieval Machine Learning

Abstract

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.

Keywords

Cite

@article{arxiv.2008.00103,
  title  = {F*: An Interpretable Transformation of the F-measure},
  author = {David J. Hand and Peter Christen and Nishadi Kirielle},
  journal= {arXiv preprint arXiv:2008.00103},
  year   = {2021}
}

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7 pages