[Website](https://catboost.ai) | [Documentation](https://catboost.ai/docs/) | [Tutorials](https://catboost.ai/docs/concepts/tutorials.html) | [Installation](https://catboost.ai/docs/concepts/installation.html) | [Release Notes](https://github.com/catboost/catboost/releases) [](https://github.com/catboost/catboost/blob/master/LICENSE) [](https://badge.fury.io/py/catboost) [](https://anaconda.org/conda-forge/catboost) [](https://github.com/catboost/catboost/issues) [](https://t.me/catboost_en) [](https://twitter.com/CatBoostML) CatBoost is a machine learning method based on [gradient boosting](https://en.wikipedia.org/wiki/Gradient_boosting) over decision trees. Main advantages of CatBoost: -------------- - Superior quality [compared](https://github.com/catboost/benchmarks/blob/master/README.md) with other GBDT libraries on many datasets. - Best-in-class [prediction](https://catboost.ai/docs/concepts/c-plus-plus-api.html) speed. - Support for both [numerical and categorical](https://catboost.ai/docs/concepts/algorithm-main-stages.html) features. - Fast GPU and multi-GPU support for out-of-the box training. - Built-in [visualization tools](https://catboost.ai/docs/features/visualization.html). - Fast and reproducible distributed training with [Apache Spark](https://catboost.ai/en/docs/concepts/spark-overview) and [CLI](https://catboost.ai/en/docs/concepts/cli-distributed-learning). Get Started and Documentation -------------- All CatBoost documentation is available [here](https://catboost.ai/docs/). Install CatBoost by following the guide for the * [Python package](https://catboost.ai/en/docs/concepts/python-installation) * [R-package](https://catboost.ai/en/docs/concepts/r-installation) * [Сommand line](https://catboost.ai/en/docs/concepts/cli-installation) * [Package for Apache Spark](https://catboost.ai/en/docs/concepts/spark-installation) Next you may want to explore: * [Tutorials](https://github.com/catboost/tutorials/#readme) * [Training modes and metrics](https://catboost.ai/docs/concepts/loss-functions.html) * [Cross-validation](https://catboost.ai/docs/features/cross-validation.html#cross-validation) * [Parameters tuning](https://catboost.ai/docs/concepts/parameter-tuning.html) * [Feature importance calculation](https://catboost.ai/docs/features/feature-importances-calculation.html) * [Regular](https://catboost.ai/docs/features/prediction.html#prediction) and [staged](https://catboost.ai/docs/features/staged-prediction.html#staged-prediction) predictions * CatBoost for Apache Spark videos: [Introduction](https://youtu.be/47-mAVms-b8) and [Architecture](https://youtu.be/nrGt5VKZpzc) If you cannot open documentation in your browser try adding yastatic.net and yastat.net to the list of allowed domains in your Privacy Badger. CatBoost models in production -------------- If you want to evaluate CatBoost model in your application read [model api documentation](https://github.com/catboost/catboost/tree/master/catboost/CatboostModelAPI.md). Questions and bug reports -------------- * For reporting bugs please use the [catboost/bugreport](https://github.com/catboost/catboost/issues) page. * Ask a question on [CatBoost GitHub Discussions Q&A forum](https://github.com/catboost/catboost/discussions/categories/q-a). * Ask a question on [Stack Overflow](https://stackoverflow.com/questions/tagged/catboost) with the catboost tag, we monitor this for new questions. * Seek prompt advice at [Telegram group](https://t.me/catboost_en) or Russian-speaking [Telegram chat](https://t.me/catboost_ru) Help to Make CatBoost Better ---------------------------- * Check out [open problems](https://github.com/catboost/catboost/blob/master/open_problems/open_problems.md) and [help wanted issues](https://github.com/catboost/catboost/labels/help%20wanted) to see what can be improved, or open an issue if you want something. * Add your stories and experience to [Awesome CatBoost](AWESOME.md). * [Instructions for contributors](https://github.com/catboost/catboost/blob/master/CONTRIBUTING.md). News -------------- Latest news are published on [twitter](https://twitter.com/catboostml). Reference Paper ------- Anna Veronika Dorogush, Andrey Gulin, Gleb Gusev, Nikita Kazeev, Liudmila Ostroumova Prokhorenkova, Aleksandr Vorobev ["Fighting biases with dynamic boosting"](https://arxiv.org/abs/1706.09516). arXiv:1706.09516, 2017. Anna Veronika Dorogush, Vasily Ershov, Andrey Gulin ["CatBoost: gradient boosting with categorical features support"](http://learningsys.org/nips17/assets/papers/paper_11.pdf). Workshop on ML Systems at NIPS 2017. License ------- © YANDEX LLC, 2017-2026. Licensed under the Apache License, Version 2.0. See LICENSE file for more details.