mlsanityData to 7 Oct 2026

What the F-measure doesn't measure: Features, Flaws, Fallacies and Fixes

arXiv 1503.06410 ↗First posted 22 Mar 2015cs.IR cs.CL cs.LGcited by 10

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Abstract (arXiv)

The F-measure or F-score is one of the most commonly used single number measures in Information Retrieval, Natural Language Processing and Machine Learning, but it is based on a mistake, and the flawed assumptions render it unsuitable for use in most contexts! Fortunately, there are better alternatives.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Mar 2015.

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Citing papers per year: 2021: 2; 2022: 1; 2023: 1; 2024: 3; 2025: 3.

10 papers.

  1. 2022Evaluation Gaps in Machine Learning Practicecited by 48
  2. 2024A Closer Look at Classification Evaluation Metrics and a Critical Reflection of Common Evaluation Practicecited by 26
  3. 2023Matching Exemplar as Next Sentence Prediction (MeNSP): Zero-shot Prompt Learning for Automatic Scoring in Science Educationcited by 18
  4. 2025Shifting the Paradigm: A Diffeomorphism Between Time Series Data Manifolds for Achieving Shift-Invariancy in Deep Learningcited by 5
  5. 2021A Real Use Case of Semi-Supervised Learning for Mammogram Classification in a Local Clinic of Costa Ricacited by 3
  6. 2024Estimating Agreement by Chance for Sequence Annotationcited by 2
  7. 2021Identification of Metallic Objects using Spectral Magnetic Polarizability Tensor Signatures: Object Classificationcited by 0
  8. 2024Exploration and Evaluation of Bias in Cyberbullying Detection with Machine Learningcited by 0
  9. 2025Identity resolution of software metadata using Large Language Modelscited by 0
  10. 2025Multi-Scale Deep Learning for Colon Histopathology: A Hybrid Graph-Transformer Approachcited by 0