@inproceedings{yacouby-axman-2020-probabilistic,
    title = "Probabilistic Extension of Precision, Recall, and F1 Score for More Thorough Evaluation of Classification Models",
    author = "Yacouby, Reda  and
      Axman, Dustin",
    editor = "Eger, Steffen  and
      Gao, Yang  and
      Peyrard, Maxime  and
      Zhao, Wei  and
      Hovy, Eduard",
    booktitle = "Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2020.eval4nlp-1.9/",
    doi = "10.18653/v1/2020.eval4nlp-1.9",
    pages = "79--91",
    abstract = "In pursuit of the perfect supervised NLP classifier, razor thin margins and low-resource test sets can make modeling decisions difficult. Popular metrics such as Accuracy, Precision, and Recall are often insufficient as they fail to give a complete picture of the model`s behavior. We present a probabilistic extension of Precision, Recall, and F1 score, which we refer to as confidence-Precision (cPrecision), confidence-Recall (cRecall), and confidence-F1 (cF1) respectively. The proposed metrics address some of the challenges faced when evaluating large-scale NLP systems, specifically when the model`s confidence score assignments have an impact on the system`s behavior. We describe four key benefits of our proposed metrics as compared to their threshold-based counterparts. Two of these benefits, which we refer to as robustness to missing values and sensitivity to model confidence score assignments are self-evident from the metrics' definitions; the remaining benefits, generalization, and functional consistency are demonstrated empirically."
}