Catboost

CatBoost[6] is an open-source software library developed by Yandex. It provides a gradient boosting framework which attempts to solve for Categorical features using a permutation driven alternative compared to the classical algorithm.[7] It works on Linux, Windows, macOS, and is available in Python,[8] R,[9] and models built using catboost can be used for predictions in C++, Java,[10] C#, Rust, Core ML, ONNX, and PMML. The source code is licensed under Apache License and available on GitHub.[6]

CatBoost
Original author(s)Andrey Gulin:[1] / Yandex
Developer(s)Yandex and CatBoost Contributors[2]
Initial releaseJuly 18, 2017 (2017-07-18)[3][4]
Stable release
0.24.1[5] / August 27, 2020 (2020-08-27)
Written inPython, R, C++, Java
Operating systemLinux, macOS, Windows
TypeMachine learning
LicenseApache License 2.0
Websitecatboost.ai

Features

CatBoost has gained popularity compared to other gradient boosting algorithms primarily due to the following features[11]

  • Ordered Boosting to overcome over fitting[7]
  • Native handling for categorical features[12]
  • Using Oblivious Trees or Symmetric Trees for faster execution[13]

See also

References
  1. "Andrey Gulin - People - Research at Yandex". research.yandex.com.
  2. "catboost/catboost". GitHub.
  3. "Yandex open sources CatBoost, a gradient boosting machine learning library". TechCrunch. Retrieved 2020-08-30.
  4. Yegulalp, Serdar (2017-07-18). "Yandex open sources CatBoost machine learning library". InfoWorld. Retrieved 2020-08-30.
  5. "Release 0.24.1 · catboost/catboost". GitHub. Retrieved 2020-08-30.
  6. "catboost/catboost". August 30, 2020 – via GitHub.
  7. Prokhorenkova, Liudmila; Gusev, Gleb; Vorobev, Aleksandr; Dorogush, Anna Veronika; Gulin, Andrey (2019-01-20). "CatBoost: unbiased boosting with categorical features". arXiv:1706.09516 [cs.LG].
  8. "Python Package Index PYPI: catboost". Retrieved 2020-08-20.
  9. "Conda force package catboost-r". Retrieved 2020-08-30.
  10. "Maven Repository: ai.catboost » catboost-prediction". mvnrepository.com. Retrieved 2020-08-30.
  11. Joseph, Manu (2020-02-29). "The Gradient Boosters V: CatBoost". Deep & Shallow. Retrieved 2020-08-30.
  12. Dorogush, Anna Veronika; Ershov, Vasily; Gulin, Andrey (2018-10-24). "CatBoost: gradient boosting with categorical features support". arXiv:1810.11363 [cs.LG].
  13. "CatBoost Enables Fast Gradient Boosting on Decision Trees Using GPUs". NVIDIA Developer Blog. 2018-12-13. Retrieved 2020-08-30.
This article is issued from Wikipedia. The text is licensed under Creative Commons - Attribution - Sharealike. Additional terms may apply for the media files.