قارن الطرق

	كاتي بوست المُنتَظِم (Regularized CatBoost)	XGBoost
المجال	تعلم الآلة	تعلم الآلة
العائلة	Machine learning	Machine learning
سنة النشأة	2018	2016
صاحب الطريقة	Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (Yandex Research)	Chen, T. & Guestrin, C.
النوع	Regularized gradient boosting ensemble	Ensemble (gradient-boosted decision trees)
المصدر التأسيسي	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, 785–794.
الأسماء البديلة	CatBoost with regularization, regularized categorical boosting, CatBoost L2 regularization, penalized CatBoost	XGBoost, extreme gradient boosting, scalable tree boosting
ذات صلة	5	5
الملخص	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.	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.