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.
recommendation systems, weather forecasting, and many others [2, 15, 17, 18]. It is backed by strong theoretical results that explain how strong predictors can be built by iterative combining weaker models (base predictors) via a greedy procedure that …
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