方法对比

	正则化 CatBoost	梯度提升（Gradient Boosting）
领域	机器学习	机器学习
方法族	Machine learning	Machine learning
起源年份	2018	2001
提出者	Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (Yandex Research)	Friedman, J. H.
类型	Regularized gradient boosting ensemble	Ensemble (sequential boosting of 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. Advances in Neural Information Processing Systems, 31.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.
别名	CatBoost with regularization, regularized categorical boosting, CatBoost L2 regularization, penalized CatBoost	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
相关	5	5
摘要	Regularized CatBoost applies explicit regularization controls — L2 leaf regularization, tree depth constraints, shrinkage rate, and model size penalties — on top of CatBoost's ordered gradient boosting framework, reducing overfitting while retaining CatBoost's native handling of categorical features and its low prediction latency on tabular datasets.	Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost.