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CatBoost

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About

CatBoost is a high-performance open-source library designed for gradient boosting on decision trees. It provides great quality results without extensive parameter tuning, supporting categorical features directly to improve training outcomes. CatBoost includes an efficient GPU implementation for training models on large datasets and offers improved accuracy with a novel gradient-boosting scheme. The library is suitable for various applications, including search and recommendation systems, and is used by companies like Yandex and CERN.

Platform
Web
Keywords
machine learningopen sourcedecision treesgradient boostingcatboost
Task
gradient boosting
Features

• great quality without parameter tuning

• fast and scalable gpu version

• improved accuracy

• categorical features support

• fast prediction

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