Uporedite metode

	Регуларизовани CatBoost	Регуларизовано појачање градијента
Oblast	Mašinsko učenje	Mašinsko učenje
Porodica	Machine learning	Machine learning
Godina nastanka	2018	2001 (gradient boosting); 2016 (explicit L1/L2 regularization in XGBoost)
Tvorac	Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (Yandex Research)	Chen, T. & Guestrin, C. (building on Friedman, J. H.)
Tip	Regularized gradient boosting ensemble	Regularized ensemble (additive tree model)
Temeljni izvor	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.	Chen, T. & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.
Drugi nazivi	CatBoost with regularization, regularized categorical boosting, CatBoost L2 regularization, penalized CatBoost	penalized gradient boosting, shrinkage-regularized boosting, XGBoost-style regularization, L1/L2 gradient boosting
Srodne	5	6
Sažetak	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.	Regularized gradient boosting extends the classic additive tree ensemble (Friedman 2001) by embedding L1 and L2 penalty terms directly into the training objective, along with a complexity penalty on tree size. Popularized by XGBoost (Chen & Guestrin 2016), this framework reduces overfitting and improves generalization compared to unpenalized boosting, while retaining the method's characteristic accuracy on tabular data.