方法对比

	均方误差 (MSE)	赤池信息量准则 (AIC)
领域	模型评估	模型评估
方法族	MCDM	MCDM
起源年份	1809	1974
提出者	Carl Friedrich Gauss	Hirotugu Akaike
类型	Squared-error loss function	Model selection metric
开创性文献	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.
别名	MSE, L2 error, quadratic error	AIC
相关	4	4
摘要	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.