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Repository files navigation

Website | Documentation | Tutorials | Installation | Release Notes

GitHub licensePyPI versionConda VersionGitHub issuesTelegramTwitter

CatBoost is a machine learning method based on gradient boosting over decision trees.

Main advantages of CatBoost:

Get Started and Documentation

All CatBoost documentation is available here.

Install CatBoost by following the guide for the

Next you may want to investigate:

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.

Questions and bug reports

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, 2017-2024. Licensed under the Apache License, Version 2.0. See LICENSE file for more details.

About

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.

catboost.ai

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Repository files navigation
Website |
Documentation |
Tutorials |
Installation |
Release Notes
GitHub licensePyPI versionConda VersionGitHub issuesTelegramTwitter
CatBoost is a machine learning method based on gradient boosting over decision trees.
Main advantages of CatBoost:
Get Started and Documentation
All CatBoost documentation is available here.
Install CatBoost by following the guide for the
Next you may want to investigate:
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.
Questions and bug reports
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, 2017-2024. Licensed under the Apache License, Version 2.0. See LICENSE file for more details.
About
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.
catboost.ai
Resources
Readme
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Repository files navigation
Website |
Documentation |
Tutorials |
Installation |
Release Notes
GitHub licensePyPI versionConda VersionGitHub issuesTelegramTwitter
CatBoost is a machine learning method based on gradient boosting over decision trees.
Main advantages of CatBoost:
Get Started and Documentation
All CatBoost documentation is available here.
Install CatBoost by following the guide for the
Next you may want to investigate:
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.
Questions and bug reports
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, 2017-2024. Licensed under the Apache License, Version 2.0. See LICENSE file for more details.
About
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.
catboost.ai
Resources
Readme
License
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Repository files navigation
Website |
Documentation |
Tutorials |
Installation |
Release Notes
GitHub licensePyPI versionConda VersionGitHub issuesTelegramTwitter
CatBoost is a machine learning method based on gradient boosting over decision trees.
Main advantages of CatBoost:
Get Started and Documentation
All CatBoost documentation is available here.
Install CatBoost by following the guide for the
Next you may want to investigate:
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.
Questions and bug reports
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, 2017-2024. Licensed under the Apache License, Version 2.0. See LICENSE file for more details.
About
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.
catboost.ai
Resources
Readme
License
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CatBoost is a machine learning method based on gradient boosting over decision trees.
Main advantages of CatBoost:
Get Started and Documentation
All CatBoost documentation is available here.
Install CatBoost by following the guide for the
Next you may want to investigate:
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.
Questions and bug reports
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, 2017-2024. Licensed under the Apache License, Version 2.0. See LICENSE file for more details.
About
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.
catboost.ai
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GitHub licensePyPI versionConda VersionGitHub issuesTelegramTwitter
CatBoost is a machine learning method based on gradient boosting over decision trees.
Main advantages of CatBoost:
Get Started and Documentation
All CatBoost documentation is available here.
Install CatBoost by following the guide for the
Next you may want to investigate:
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.
Questions and bug reports
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, 2017-2024. Licensed under the Apache License, Version 2.0. See LICENSE file for more details.
About
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.
catboost.ai
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CatBoost is a machine learning method based on gradient boosting over decision trees.
Main advantages of CatBoost:
Get Started and Documentation
All CatBoost documentation is available here.
Install CatBoost by following the guide for the
Next you may want to investigate:
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.
Questions and bug reports
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, 2017-2024. Licensed under the Apache License, Version 2.0. See LICENSE file for more details.
About
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.
catboost.ai
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Readme
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Repository files navigation
Website |
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Release Notes
GitHub licensePyPI versionConda VersionGitHub issuesTelegramTwitter
CatBoost is a machine learning method based on gradient boosting over decision trees.
Main advantages of CatBoost:
Get Started and Documentation
All CatBoost documentation is available here.
Install CatBoost by following the guide for the
Next you may want to investigate:
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.
Questions and bug reports
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, 2017-2024. Licensed under the Apache License, Version 2.0. See LICENSE file for more details.
About
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.
catboost.ai
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Readme
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