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
Variants
Classifier Calibration
Parents
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University of California San Diego
San Diego, United States
5 h-index
8 publications
3.9K citations
Carnegie Mellon University
Pittsburgh, United States
4 h-index
8 publications
474 citations

Cornell University
Ithaca, United States
3 h-index
8 publications
2.1K citations
University of North Carolina at Chapel Hill
Chapel Hill, United States
3 h-index
4 publications
83 citations
Stanford University
Stanford, United States
3 h-index
6 publications
451 citations
University College London
London, United Kingdom
3 h-index
7 publications
280 citations

University of Manchester
Manchester, United Kingdom
2 h-index
3 publications
170 citations
The University of Texas at Austin
Austin, United States
2 h-index
2 publications
70 citations
1–8 of 154
Repository · 10 publications · 860 citations
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5 publications · 412 citations
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4 publications · 331 citations
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3 publications · 104 citations
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Conference · 2 publications · 145 citations
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