方法对比

	均方根误差 (RMSE)	均方误差 (MSE)
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	Distance-based evaluation metric	Squared-error loss function
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	RMSE, RMS error, quadratic mean error	MSE, L2 error, quadratic error
相关	4	4
摘要	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.	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.