krausefx / catboost

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.

Website | Documentation | Tutorials | Installation | Release Notes

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.
• Fast and reproducible distributed training with Apache Spark and CLI.

Get Started and Documentation

All CatBoost documentation is available here.

Install CatBoost by following the guide for the

• Python package
• R-package
• Сommand line
• Package for Apache Spark

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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Catboost models in production

If you want to evaluate Catboost model in your application read model api documentation.

Questions and bug reports

• For reporting bugs please use the catboost/bugreport page.
• 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

Help to Make CatBoost Better

• 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.
• 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

Latest news are published on twitter.

Reference Paper

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.

License

© YANDEX LLC, 2017-2023. Licensed under the Apache License, Version 2.0. See LICENSE file for more details.