Log Loss
Log Loss is a probabilistic loss function used in classification tasks that measures the performance of a model by penalizing incorrect predictions based on their deviation from the true class labels, where the penalty increases logarithmically as the predicted probability diverges from the actual outcome.
118
Publications
6.7K
Citations
357
Authors
170
Institutions
Publications per year
2017–2026
62
Parents
118
A Comprehensive Survey of Loss Functions in Machine Learning
Qi Wang, Yue Ma, Kun Zhao et al. · Annals of Data Science · 2020 · 693 citations
An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks
Andrew Churcher, Rehmat Ullah, Jawad Ahmad et al. · Sensors · 2021 · 424 citations · Full text
1–5 of 118
357
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National Institute of Allergy and Infectious Diseases, United States
4 publications · 221 citations
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National Institute of Allergy and Infectious Diseases, United States
4 publications · 221 citations
4
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3
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3
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1–5 of 357
170
University of California, Santa Cruz
Santa Cruz, United States
5 h-index
6 publications
430 citations
National Institutes of Health
Bethesda, United States
5 h-index
17 publications
919 citations
National Institute of Allergy and Infectious Diseases
Bethesda, United States
4 h-index
16 publications
884 citations

Johns Hopkins University
Baltimore, United States
3 h-index
5 publications
137 citations
Carnegie Mellon University
Pittsburgh, United States
3 h-index
4 publications
105 citations
Stanford University
Stanford, United States
3 h-index
6 publications
288 citations
Centrum Wiskunde & Informatica
Amsterdam, The Netherlands
3 h-index
3 publications
72 citations
Microsoft (United States)
Redmond, United States
2 h-index
2 publications
53 citations
1–8 of 170
Repository · 18 publications · 679 citations
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4 publications · 221 citations
4
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3 publications · 351 citations
3
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2 publications · 64 citations
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Repository · 2 publications · 72 citations
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