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	Errore Assoluto Medio (MAE)	Errore Quadratico Medio (MSE)
Campo	Valutazione dei modelli	Valutazione dei modelli
Famiglia	MCDM	MCDM
Anno di origine	1799	1809
Ideatore	Pierre-Simon Laplace	Carl Friedrich Gauss
Tipo	Robust distance-based metric	Squared-error loss function
Fonte seminale	Laplace, P. S. (1799). Traité de Mécanique Céleste. Paris: J.B.M. Duprat.	Gauss, C. F. (1809). Theoria Motus Corporum Coelestium in Sectionibus Conicis Solem Ambientium. Hamburg: Perthes and Besser.
Alias	MAE, L1 error, mean absolute deviation	MSE, L2 error, quadratic error
Correlati	3	4
Sintesi	Mean Absolute Error is a robust metric that measures the average absolute magnitude of prediction errors in regression models. Dating back to Pierre-Simon Laplace's work on observational errors (1799), MAE quantifies typical prediction deviation by averaging the absolute differences between observed and predicted values.	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.