• Corpus ID: 26037613

CatBoost: gradient boosting with categorical features support

@article{Dorogush2018CatBoostGB,
  title={CatBoost: gradient boosting with categorical features support},
  author={Anna Veronika Dorogush and Vasily Ershov and Andrey Gulin},
  journal={ArXiv},
  year={2018},
  volume={abs/1810.11363}
}
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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