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
- If you want to evaluate Catboost model in your application read model api documentation.
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 CONTRIBUTING.md
Instructions for contributors can be found here.
- News are published on twitter.
Questions and bug reports
- For reporting bugs please use the catboost/bugreport page.
- Ask your question about CatBoost on Stack Overflow.
© YANDEX LLC, 2017-2018. Licensed under the Apache License, Version 2.0. See LICENSE file for more details.