Papers › CatBoost: gradient boosting with categorical features support

CatBoost: gradient boosting with categorical features support

24 Oct 2018arXiv:1810.11363archive 2025-07-28

Anna Veronika Dorogush, Vasily Ershov, Andrey Gulin

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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