방법 비교

	평균 제곱 오차 (MSE)	평균 제곱근 오차 (Root Mean Squared Error, RMSE)
분야	모델 평가	모델 평가
계열	MCDM	MCDM
기원 연도	1809	1809
창시자	Carl Friedrich Gauss	Carl Friedrich Gauss
유형	Squared-error loss function	Distance-based evaluation metric
원전	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.
별칭	MSE, L2 error, quadratic error	RMSE, RMS error, quadratic mean error
관련	4	4
요약	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.