So sánh phương pháp

	Tối ưu hóa Gradient Ngẫu nhiên (Stochastic Gradient Descent - SGD)	XGBoost
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	1951	2016
Người khởi xướng	Robbins, H. & Monro, S.	Chen, T. & Guestrin, C.
Loại	First-order iterative optimization algorithm	Ensemble (gradient-boosted decision trees)
Công trình gốc	Robbins, H. & Monro, S. (1951). A Stochastic Approximation Method. The Annals of Mathematical Statistics, 22(3), 400–407.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Tên gọi khác	SGD, online gradient descent, incremental gradient descent, mini-batch gradient descent	XGBoost, extreme gradient boosting, scalable tree boosting
Liên quan	3	5
Tóm tắt	Stochastic Gradient Descent (SGD) is a first-order iterative optimization algorithm, rooted in the stochastic approximation framework introduced by Robbins and Monro in 1951, that minimizes an objective function by updating model parameters using the gradient computed on a single randomly selected training example (or a small mini-batch) at each step. It is the core optimization engine behind modern machine learning and deep learning, enabling the training of models on datasets too large to fit in memory.	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.