방법 비교

	평균 제곱 오차 (MSE)	평균 절대 오차 (MAE)
분야	모델 평가	모델 평가
계열	MCDM	MCDM
기원 연도	1809	1799
창시자	Carl Friedrich Gauss	Pierre-Simon Laplace
유형	Squared-error loss function	Robust distance-based metric
원전	Gauss, C. F. (1809). Theoria Motus Corporum Coelestium in Sectionibus Conicis Solem Ambientium. Hamburg: Perthes and Besser.	Laplace, P. S. (1799). Traité de Mécanique Céleste. Paris: J.B.M. Duprat.
별칭	MSE, L2 error, quadratic error	MAE, L1 error, mean absolute deviation
관련	4	3
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.	Mean Absolute Error is a robust metric that measures the average absolute magnitude of prediction errors in regression models. Dating back to Pierre-Simon Laplace's work on observational errors (1799), MAE quantifies typical prediction deviation by averaging the absolute differences between observed and predicted values.