A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
CatBoost is a machine learning method based on gradient boosting over decision trees.
Main advantages of CatBoost: - Superior quality when compared with other GBDT libraries on many datasets. - Best in class prediction speed. - Support for both numerical and categorical features. - Fast GPU and multi-GPU support for training out of the box. - Visualization tools included.
All CatBoost documentation is available here.
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If you want to evaluate Catboost model in your application read model api documentation.
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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.
© YANDEX LLC, 2017-2019. Licensed under the Apache License, Version 2.0. See LICENSE file for more details.