CatBoost: gradient boosting with categorical features support

Anna Veronika Dorogush, Vasily Ershov, Andrey Gulin
10/24/2018
cs.LGcs.MSstat.ML

Official code: catboost/catboost (9,133 stars, Apache-2.0, last commit 2026-04-29).

Abstract

In this paper we present CatBoost, a new open-sourced gradient boosting library that successfully handles categorical features and outperforms existing publicly available implementations of gradient boosting in terms of quality on a set of popular publicly available datasets. The library has a GPU implementation of learning algorithm and a CPU implementation of scoring algorithm, which are significantly faster than other gradient boosting libraries on ensembles of similar sizes.

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Code Implementations(1)

catboost/catboostOfficial100%
Apache-2.0

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.

9,1331,345C, RJul 18, 20175 months agoApache-2.0
big-datacatboostcategorical-featurescoremlcuda+13 more

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Cite this paper

@article{dorogush2018catboost,
  title  = {CatBoost: gradient boosting with categorical features support},
  author = {Anna Veronika Dorogush and Vasily Ershov and Andrey Gulin},
  year   = {2018},
  eprint = {1810.11363},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1810.11363}
}

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