CatBoost
yandex open source gradient boosting framework on decision trees
CatBoost is an open-source software library developed by Yandex. It provides a gradient boosting framework which, among other features, attempts to solve for categorical features using a permutation-driven alternative to the classical algorithm. It works on Linux, Windows, macOS, and is available in
Python,
R, and models built using CatBoost can be used for predictions in C++, Java, C#, Rust, Core ML, ONNX, and PMML. The source code is licensed under Apache License and available on GitHub.
InfoWorld magazine awarded the library "The best machine learning tools" in 2017. along with TensorFlow, Pytorch, XGBoost and 8 other libraries.
Kaggle listed CatBoost as one of the most frequently used machine learning (ML) frameworks in the world. It was listed as the top-8 most frequently used ML framework in the 2020 survey and as the top-7 most frequently used ML framework in the 2021 survey.
As of April 2022, CatBoost is installed about 100000 times per day from PyPI repository
Features
CatBoost has gained popularity compared to other gradient boosting algorithms primarily due to the following features
Native handling for categorical features
Fast GPU training
Visualizations and tools for model and feature analysis
Using oblivious trees or symmetric trees for faster execution
Ordered boosting to overcome overfitting
History
In 2009 Andrey Gulin developed MatrixNet, a proprietary gradient boosting library that was used in Yandex to rank search results.
Since 2009 MatrixNet has been used in different projects at Yandex, including recommendation systems and weather prediction.
In 2014–2015 Andrey Gulin worked with a team of researchers to start a new project called Tensornet which was aimed at solving the problem of "how to work with categorical data".
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