CatBoost: gradient boosting with categorical features support
This provides a more efficient and effective gradient boosting solution for machine learning practitioners dealing with categorical data, though it is incremental in nature.
The authors tackled the problem of gradient boosting with categorical features by introducing CatBoost, a new library that outperforms existing implementations in quality on popular datasets and offers faster GPU learning and CPU scoring.
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.