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CatBoost

swMATH ID: 31560
Software Authors: Anna Veronika Dorogush, Vasily Ershov, Andrey Gulin
Description: CatBoost: gradient boosting with categorical features support. 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.
Homepage: https://catboost.ai/
Source Code:  https://github.com/catboost/catboost
Keywords: Machine Learning; arXiv_cs.LG; arXiv_cs.MS; arXiv_stat.ML; gradient boosting; categorical features
Related Software: XGBoost; LightGBM; Scikit; GitHub; shap; glmnet; xgboost; UCI-ml; ImageNet; AppliedPredictiveModeling; rpart; LambdaMART; AlexNet; Adam; Keras; ranger; R; randomForest; TensorFlow; FFORMA
Cited in: 23 Documents

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1 Publication describing the Software Year
CatBoost: gradient boosting with categorical features support arXiv
Anna Veronika Dorogush, Vasily Ershov, Andrey Gulin
2018

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