Comparer des méthodes

	Critère d'information d'Akaike (AIC)	Erreur quadratique moyenne (EQM)
Domaine	Évaluation de modèles	Évaluation de modèles
Famille	MCDM	MCDM
Année d'origine	1974	1809
Auteur d'origine	Hirotugu Akaike	Carl Friedrich Gauss
Type	Model selection metric	Squared-error loss function
Source fondatrice	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.
Alias	AIC	MSE, L2 error, quadratic error
Apparentées	4	4
Résumé	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.