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CatBoost is a machine learning method based on gradient boosting over decision trees.
- 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.
- Fast and reproducible distributed training with Apache Spark and CLI.
All CatBoost documentation is available here.
Install CatBoost by following the guide for the
Next you may want to investigate:
- Tutorials
- Training modes and metrics
- Cross-validation
- Parameters tuning
- Feature importance calculation
- Regular and staged predictions
- CatBoost for Apache Spark videos: Introduction and Architecture
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If you want to evaluate CatBoost model in your application read model api documentation.
- For reporting bugs please use the catboost/bugreport page.
- Ask a question on CatBoost GitHub Discussions Q&A forum.
- Ask a question on Stack Overflow with the catboost tag, we monitor this for new questions.
- Seek prompt advice at Telegram group or Russian-speaking Telegram chat
- Check out open problems and help wanted issues to see what can be improved, or open an issue if you want something.
- Add your stories and experience to Awesome CatBoost.
- Instructions for contributors.
Latest news are published on twitter.
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, 2017-2025. Licensed under the Apache License, Version 2.0. See LICENSE file for more details.