CatBoost for big data: an interdisciplinary review

CatBoost for big data: an interdisciplinary review

Abstract Gradient Boosted Decision Trees (GBDT’s) are a powerful tool for classification and regression tasks in Big Data. Researchers should be familiar with the strengths and weaknesses of current implementations of GBDT’s in order to use them effectively and make successful contributions. CatBoos...

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Bibliographic Details
Main Authors: John T. Hancock, Taghi M. Khoshgoftaar
Format: Article
Language:English
Published: SpringerOpen 2020-11-01
Series:Journal of Big Data
Subjects:
CatBoost
Big data
Categorical variable encoding
Ensemble methods
Machine learning
Decision tree
Online Access:http://link.springer.com/article/10.1186/s40537-020-00369-8

Internet

http://link.springer.com/article/10.1186/s40537-020-00369-8

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