Score Calibration

Score Calibration is a probabilistic method that adjusts raw model output scores to reflect true likelihoods of predicted events, ensuring that the confidence of predictions aligns with their empirical accuracy.

46

Publications

2.2K

Citations

188

Authors

64

Institutions

Publications per year

2017–2026

18

Authors

188

  1. JH
    John H. L. Hansen

    The University of Texas at Dallas, United States

    5 publications · 249 citations

    5

    h-index

  2. RS
    Rahim Saeidi

    Radboud University Nijmegen, Netherlands

    3 publications · 174 citations

    3

    h-index

  3. KD
    Kris Demuynck

    KU Leuven, Belgium

    2 publications · 69 citations

    2

    h-index

  4. SR
    Shivesh Ranjan

    The University of Texas at Dallas, United States

    2 publications · 95 citations

    2

    h-index

  5. JG
    Jean‐Luc Gauvain

    Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur, France

    2 publications · 28 citations

    2

    h-index

1–5 of 188

Venues

  1. The American Statistician

    2 publications · 83 citations

    2

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  2. 2

    h-index

  3. arXiv (Cornell University)

    Repository · 2 publications · 33 citations

    2

    h-index

  4. Research Evaluation

    1 publications · 49 citations

    1

    h-index

  5. Journal of Proteome Research

    JPR · 1 publications · 31 citations

    1

    h-index

1–5