방법 비교

	평균 제곱 오차 (MSE)	아카이케 정보량 기준 (AIC)
분야	모델 평가	모델 평가
계열	MCDM	MCDM
기원 연도	1809	1974
창시자	Carl Friedrich Gauss	Hirotugu Akaike
유형	Squared-error loss function	Model selection metric
원전	Gauss, C. F. (1809). Theoria Motus Corporum Coelestium in Sectionibus Conicis Solem Ambientium. Hamburg: Perthes and Besser.	Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716-723.
별칭	MSE, L2 error, quadratic error	AIC
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.	The Akaike Information Criterion is an information-theoretic measure for model selection that balances goodness of fit against model complexity. Introduced by Hirotugu Akaike in 1974, AIC estimates the relative quality of models for a given dataset, penalizing additional parameters to prevent overfitting.