方法对比

	均方误差 (MSE)	均方根误差 (RMSE)
领域	模型评估	模型评估
方法族	MCDM	MCDM
起源年份	1809	1809
提出者	Carl Friedrich Gauss	Carl Friedrich Gauss
类型	Squared-error loss function	Distance-based evaluation metric
开创性文献	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.
别名	MSE, L2 error, quadratic error	RMSE, RMS error, quadratic mean error
相关	4	4
摘要	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.