CatBoost: gradient boosting with categorical features support
Machine Learning
2018-10-29 v1 Mathematical Software
Machine Learning
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.
Keywords
Cite
@article{arxiv.1810.11363,
title = {CatBoost: gradient boosting with categorical features support},
author = {Anna Veronika Dorogush and Vasily Ershov and Andrey Gulin},
journal= {arXiv preprint arXiv:1810.11363},
year = {2018}
}