手法を比較

	平均二乗誤差（MSE）	赤池情報量基準 (AIC)
分野	モデル評価	モデル評価
系統	MCDM	MCDM
提唱年	1809	1974
提唱者	Carl Friedrich Gauss	Hirotugu Akaike
種類	Squared-error loss function	Model selection metric
原典	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.
別名	MSE, L2 error, quadratic error	AIC
関連	4	4
概要	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.