Transcription of CatBoost: gradient boosting with categorical features support
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CatBoost: gradient boosting with categorical features support
catboost : gradient boosting with categorical featuressupportAnna Veronika Dorogush, Vasily Ershov, Andrey GulinYandexAbstractIn this paper we present catboost , a new open-sourced gradient boosting librarythat successfully handles categorical features and outperforms existing publiclyavailable implementations of gradient boosting in terms of quality on a set ofpopular publicly available datasets. The library has a GPU implementation oflearning algorithm and a CPU implementation of scoring algorithm, which aresignificantly faster than other gradient boosting libraries on ensembles of IntroductionGradient boosting is a powerful machine-learning technique that achieves state-of-the-art resultsin a variety of practical tasks. For a number of years, it has remained the primary method forlearning problems with heterogeneous features , noisy data, and complex dependencies: web search,recommendation systems, weather forecasting, and many others [2,15,17,18].
CatBoost: gradient boosting with categorical features support Anna Veronika Dorogush, Vasily Ershov, Andrey Gulin Yandex Abstract In this paper we present CatBoost, a new open-sourced gradient boosting library
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