Võrdle meetodeid

	Ruutkeskmine viga (MSE)	Akaike informatsioonikriteerium (AIC)
Valdkond	Mudelite hindamine	Mudelite hindamine
Perekond	MCDM	MCDM
Tekkeaasta	1809	1974
Looja	Carl Friedrich Gauss	Hirotugu Akaike
Tüüp	Squared-error loss function	Model selection metric
Algallikas	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.
Rööpnimetused	MSE, L2 error, quadratic error	AIC
Seotud	4	4
Kokkuvõte	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.