Binary Calibration is a probabilistic calibration method that ensures the predicted probabilities of a binary classifier reflect the true likelihood of the positive class by aligning the average predicted probability with the observed frequency of positive outcomes within each prediction bin.
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Publications
387
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27
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21
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2017–2026
5
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Publications
10
Evidential calibration of binary SVM classifiers
Philippe Xu, Franck Davoine, Hongbin Zha et al. · International Journal of Approximate Reasoning · 2015 · 79 citations · Full text
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Authors
27
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Institutions
21
University of Tartu
Tartu, Estonia
2 h-index
3 publications
188 citations
University of California San Diego
San Diego, United States
2 h-index
4 publications
84 citations
Sorbonne Université
Paris, France
1 h-index
3 publications
237 citations
University of Massachusetts Lowell
Lowell, United States
1 h-index
1 publications
22 citations
Friedrich-Alexander-Universität Erlangen-Nürnberg
Erlangen, Germany
1 h-index
2 publications
32 citations
Peking University
Beijing, China
1 h-index
1 publications
79 citations
Cabot (United States)
Boston, United States
1 h-index
1 publications
164 citations
methodS in Patient-centered outcomes and HEalth ResEarch
Nantes, France
1 h-index
1 publications
164 citations
1–8 of 21
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- Transportation Research Record Journal of the Transportation Research Board
TRR · 1 publications · 20 citations
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- arXiv (Cornell University)
Repository · 1 publications · 164 citations
1
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- Bristol Research (University of Bristol)
Repository · 1 publications · 12 citations
1
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- International Journal of Approximate Reasoning
1 publications · 79 citations
1
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- Bulletin of the Seismological Society of America
BSSA · 1 publications · 21 citations
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