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Last Commit
Feb. 21, 2018
Jul. 18, 2017

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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.
  • Best in class inference speed.
  • Support for both numerical and categorical features.
  • Fast GPU and multi-GPU (on one node) support for training.
  • Data visualization tools included.

The following implementations are available:


  • Tutorials are avaliable here.

Catboost models in production

For contributors

  • To contribute to CatBoost you need to first read CLA text and add to your pull request, that you agree to the terms of the CLA. More information can be found in

  • Instructions for contributors can be found here.


Questions and bug reports


© YANDEX LLC, 2017-2018. Licensed under the Apache License, Version 2.0. See LICENSE file for more details.

Latest Releases
Release 0.6.2
 Feb. 9 2018
 Feb. 2 2018
Release 0.6.1
 Feb. 1 2018
Release 0.6
 Jan. 29 2018
 Jan. 9 2018