পদ্ধতির তুলনা করুন

	এনসেম্বল গ্রেডিয়েন্ট বুস্টিং	XGBoost
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2001	2016
প্রবর্তক	Friedman, J. H.	Chen, T. & Guestrin, C.
ধরন	Ensemble (sequential boosting of decision trees)	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 Machine, GBM, Gradient Tree Boosting, Stochastic Gradient Boosting	XGBoost, extreme gradient boosting, scalable tree boosting
সম্পর্কিত	6	5
সারসংক্ষেপ	Gradient Boosting is an ensemble method introduced by Jerome Friedman in 2001 that builds a strong predictive model by sequentially adding shallow decision trees, each correcting the errors of the previous ensemble. By framing the problem as gradient descent in function space, it achieves state-of-the-art accuracy on classification, regression, and ranking tasks across tabular data.	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.