قارن الطرق

	تعزيز التدرج القوي	XGBoost
المجال	تعلم الآلة	تعلم الآلة
العائلة	Machine learning	Machine learning
سنة النشأة	2001	2016
صاحب الطريقة	Friedman, J. H. (with Huber loss from Huber, P. J.)	Chen, T. & Guestrin, C.
النوع	Ensemble (boosted trees with robust loss)	Ensemble (gradient-boosted decision trees)
المصدر التأسيسي	Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
الأسماء البديلة	gradient boosting with Huber loss, robust GBM, outlier-robust boosting, robust gradient-boosted trees	XGBoost, extreme gradient boosting, scalable tree boosting
ذات صلة	6	5
الملخص	Robust Gradient Boosting is gradient boosting trained with outlier-resistant loss functions — most commonly the Huber loss or quantile (pinball) loss — instead of squared-error loss. Proposed in Friedman's seminal 2001 paper, this variant produces predictions far less distorted by extreme values or contaminated labels, while retaining the full predictive power of gradient-boosted trees.	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.