方法对比

	CatBoost	XGBoost
领域	机器学习	机器学习
方法族	Machine learning	Machine learning
起源年份	2018	2016
提出者	Prokhorenkova, L. et al. (Yandex)	Chen, T. & Guestrin, C.
类型	Gradient boosting on decision trees	Ensemble (gradient-boosted decision trees)
开创性文献	Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V. & Gulin, A. (2018). CatBoost: Unbiased Boosting with Categorical Features. In NeurIPS 2018.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
别名	CatBoost (Categorical Boosting), categorical boosting, ordered boosting, kategorik gradyan artırma	XGBoost, extreme gradient boosting, scalable tree boosting
相关	5	5
摘要	CatBoost is a gradient boosting algorithm, introduced by Prokhorenkova and colleagues at Yandex in 2018, that handles categorical variables natively and uses ordered target encoding to avoid label leakage. By building an additive ensemble of trees while permuting the data order at each iteration, it is often superior to XGBoost and LightGBM on category-heavy data.	XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions.