Krahasoni metodat

	Kriteri i Informacionit Akaike (AIC)	Gabimi Mesatar Katror (MSE)
Fusha	Vlerësimi i modeleve	Vlerësimi i modeleve
Familja	MCDM	MCDM
Viti i origjinës	1974	1809
Krijuesi	Hirotugu Akaike	Carl Friedrich Gauss
Lloji	Model selection metric	Squared-error loss function
Burimi themelues	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.
Emërtime të tjera	AIC	MSE, L2 error, quadratic error
Të lidhura	4	4
Përmbledhja	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.