  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.
