Probability Calibration

Probability Calibration is a probabilistic modeling technique that adjusts the output scores of a predictive classifier to ensure that the predicted probabilities accurately reflect the true empirical likelihood of the observed outcomes.

110

Publications

11.1K

Citations

297

Authors

154

Institutions

Publications per year

2017–2026

35

Authors

297

  1. FB
    Fergus Bolger

    University College London, United Kingdom

    4 publications · 245 citations

    4

    h-index

  2. BZ
    Bianca Zadrozny

    Universidade Federal Fluminense, Brazil

    4 publications · 2K citations

    4

    h-index

  3. PL
    Percy Liang

    Stanford University, United States

    3 publications · 232 citations

    3

    h-index

  4. TG
    Tilmann Gneiting

    University of Washington, United States

    3 publications · 954 citations

    3

    h-index

  5. CE
    Charles Elkan

    University of California San Diego, United States

    3 publications · 1.9K citations

    3

    h-index

1–5 of 297

Venues

  1. arXiv (Cornell University)

    Repository · 10 publications · 860 citations

    10

    h-index

  2. 5

    h-index

  3. Management Science

    4 publications · 331 citations

    4

    h-index

  4. Bioinformatics

    3 publications · 104 citations

    3

    h-index

  5. Neural Information Processing Systems

    Conference · 2 publications · 145 citations

    2

    h-index

1–5