Comparar métodos

	Critério de Informação de Akaike (AIC)	Erro Quadrático Médio (EQM)
Área	Avaliação de modelos	Avaliação de modelos
Família	MCDM	MCDM
Ano de origem	1974	1809
Autor original	Hirotugu Akaike	Carl Friedrich Gauss
Tipo	Model selection metric	Squared-error loss function
Fonte 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.
Outros nomes	AIC	MSE, L2 error, quadratic error
Relacionados	4	4
Resumo	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.