방법 비교

	평균 제곱 오차 (MSE)	결정 계수(R²)
분야	모델 평가	모델 평가
계열	MCDM	MCDM
기원 연도	1809	1896
창시자	Carl Friedrich Gauss	Karl Pearson
유형	Squared-error loss function	Goodness-of-fit metric
원전	Gauss, C. F. (1809). Theoria Motus Corporum Coelestium in Sectionibus Conicis Solem Ambientium. Hamburg: Perthes and Besser.	Pearson, K. (1896). Mathematical contributions to the theory of evolution. Philosophical Transactions of the Royal Society A, 187, 253-318.
별칭	MSE, L2 error, quadratic error	R², coefficient of determination, r2 score
관련	4	5
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.	The coefficient of determination, denoted R², measures the proportion of variance in the dependent variable explained by the independent variables in a regression model. Introduced by Karl Pearson in the late 19th century, R² is one of the most widely used metrics for assessing how well a model fits observed data.