So sánh phương pháp

	Sai số bình phương trung bình (MSE)	Sai số bình phương trung bình gốc (RMSE)
Lĩnh vực	Đánh giá mô hình	Đánh giá mô hình
Họ	MCDM	MCDM
Năm ra đời	1809	1809
Người khởi xướng	Carl Friedrich Gauss	Carl Friedrich Gauss
Loại	Squared-error loss function	Distance-based evaluation metric
Công trình gốc	Gauss, C. F. (1809). Theoria Motus Corporum Coelestium in Sectionibus Conicis Solem Ambientium. Hamburg: Perthes and Besser.	Gauss, C. F. (1809). Theoria Motus Corporum Coelestium in Sectionibus Conicis Solem Ambientium. Hamburg: Perthes and Besser.
Tên gọi khác	MSE, L2 error, quadratic error	RMSE, RMS error, quadratic mean error
Liên quan	4	4
Tóm tắt	Mean Squared Error is the foundational loss function for regression models, measuring the average squared deviation between predictions and observations. Originating from Gauss and Legendre's method of least squares (1805-1809), MSE is the basis for ordinary least squares regression and remains central to modern machine learning optimization.	Root Mean Squared Error is a widely used metric that measures the average magnitude of prediction errors in regression models. Originating from Carl Friedrich Gauss's work on least-squares estimation (1809), RMSE quantifies how far predictions deviate from observed values by averaging the squared differences and taking the square root.