CatBoost is a gradient boosting ensemble method that distinguishes itself by natively supporting categorical features through an efficient implementation of ordered boosting and target-based encoding to reduce overfitting.
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Publications per year
2017–2026
271
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278
CatBoost for big data: an interdisciplinary review
John Hancock, Taghi M. Khoshgoftaar · Journal Of Big Data · 2020 · 1.7K citations · Full text
CatBoost: unbiased boosting with categorical features
Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev et al. · arXiv (Cornell University) · 2017 · 1.1K citations · Full text
Evaluation of CatBoost method for prediction of reference evapotranspiration in humid regions
Guomin Huang, Lifeng Wu, Xin Ma et al. · Journal of Hydrology · 2019 · 539 citations
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Florida Atlantic University
Boca Raton, United States
8 h-index
19 publications
4.2K citations
King Saud University
Riyadh, Saudi Arabia
6 h-index
9 publications
316 citations
Central South University
Changsha, China
5 h-index
18 publications
825 citations
Shanghai Jiao Tong University
Shanghai, China
5 h-index
8 publications
334 citations
Northwest A&F University
Yangling, China
4 h-index
13 publications
836 citations
Guangdong University of Technology
Guangzhou, China
4 h-index
19 publications
564 citations
Tongji University
Shanghai, China
4 h-index
7 publications
208 citations
University of Chinese Academy of Sciences
Beijing, China
4 h-index
5 publications
150 citations
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- Scientific Reports
15 publications · 732 citations
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9 publications · 332 citations
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Repository · 8 publications · 2.6K citations
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- Journal Of Big Data
6 publications · 2.4K citations
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- IEEE Access
6 publications · 1.4K citations
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