CatBoost provides Machine Learning algorithms under gradient boost framework developed by Yandex. It supports both numerical and categorical features.

It works on Linux, Windows, and macOS systems. It provides interfaces to Python and R. Trained model can be also used in C++, Java, C+, Rust, CoreML, ONNX, PMML.


Tianqi Chen and Carlos Guestrin, XGBoost: A Scalable Tree Boosting System, In 22nd SIGKDD Conference on Knowledge Discovery and Data Mining, 2016


Anna Veronika Dorogush, Andrey Gulin, Gleb Gusev, Nikita Kazeev, Liudmila Ostroumova Prokhorenkova, Aleksandr Vorobev, Fighting biases with dynamic boosting , arXiv:1706.09516, 2017.

Anna Veronika Dorogush, Vasily Ershov, Andrey Gulin, CatBoost: gradient boosting with categorical features support, Workshop on ML Systems at NIPS 2017.


Apache-2.0 License


CatBoost GitHub repository

CatBoost Documentation

CatBoost Website

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