Catboost Encoding
CatBoost Encoding is a categorical encoding technique that belongs to the class of target-based encoding methods, distinguished by its use of ordered target statistics computed from preceding instances in a randomly permuted dataset to mitigate target leakage and reduce overfitting.
11
Publications
679
Citations
33
Authors
19
Institutions
Publications per year
2017–2026
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École de management de Lyon
Lyon, France
1 h-index
1 publications
376 citations
Universidad Internacional De La Rioja
Logroño, Spain
1 h-index
3 publications
132 citations

Vilnius University
Vilnius, Lithuania
1 h-index
2 publications
46 citations
Civil Aviation University of China
Tianjin, China
1 h-index
3 publications
39 citations

China University of Petroleum, Beijing
Beijing, China
1 h-index
2 publications
122 citations

Universidad de Alcalá
Alcalá de Henares, Spain
1 h-index
2 publications
88 citations

Amrita Vishwa Vidyapeetham
Coimbatore, India
1 h-index
2 publications
42 citations
Manouba University
Manouba, Tunisia
1 h-index
1 publications
376 citations
1–8 of 19
CAGEO · 1 publications · 61 citations
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1 publications · 376 citations
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1 publications · 54 citations
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1 publications · 28 citations
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1 publications · 13 citations
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