Compara mètodes

	Criteri d'Informació d'Akaike (AIC)	Error Quadràtic Mitjà (MSE)
Camp	Avaluació de models	Avaluació de models
Família	MCDM	MCDM
Any d'origen	1974	1809
Autor original	Hirotugu Akaike	Carl Friedrich Gauss
Tipus	Model selection metric	Squared-error loss function
Font seminal	Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716-723.	Gauss, C. F. (1809). Theoria Motus Corporum Coelestium in Sectionibus Conicis Solem Ambientium. Hamburg: Perthes and Besser.
Àlies	AIC	MSE, L2 error, quadratic error
Relacionats	4	4
Resum	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.	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.