विधियों की तुलना करें

	Robust Gradient Boosting	XGBoost
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2001	2016
प्रवर्तक	Friedman, J. H. (with Huber loss from Huber, P. J.)	Chen, T. & Guestrin, C.
प्रकार	Ensemble (boosted trees with robust loss)	Ensemble (gradient-boosted decision trees)
मौलिक स्रोत	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.
उपनाम	gradient boosting with Huber loss, robust GBM, outlier-robust boosting, robust gradient-boosted trees	XGBoost, extreme gradient boosting, scalable tree boosting
संबंधित	6	5
सारांश	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.