পদ্ধতির তুলনা করুন

	Robust Gradient Boosting	XGBoost
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2001	2016
প্রবর্তক	Friedman, J. H. (with Huber loss from Huber, P. J.)	Chen, T. & Guestrin, C.
ধরন	Ensemble (boosted trees with robust loss)	Ensemble (gradient-boosted decision trees)
মৌলিক উৎস	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.
অপর নাম	gradient boosting with Huber loss, robust GBM, outlier-robust boosting, robust gradient-boosted trees	XGBoost, extreme gradient boosting, scalable tree boosting
সম্পর্কিত	6	5
সারসংক্ষেপ	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.