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Last Commit
Dec. 13, 2017
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 libraries.
  • Support for both numerical and categorical features.
  • Data visualization tools included.

The following implementations are available:


  • Tutorials are avaliable here.

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.


  • News will be published on twitter.

Questions and bug reports


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

Latest Releases
Release 0.5
 Nov. 29 2017
Release 0.4.1
 Nov. 23 2017
Release 0.4
 Nov. 22 2017
Release 0.3
 Nov. 2 2017
Release 0.2
 Sep. 14 2017