Compara mètodes

	Error Absolut Mitjà (MAE)	Error Quadràtic Mitjà (MSE)
Camp	Avaluació de models	Avaluació de models
Família	MCDM	MCDM
Any d'origen	1799	1809
Autor original	Pierre-Simon Laplace	Carl Friedrich Gauss
Tipus	Robust distance-based metric	Squared-error loss function
Font seminal	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.
Àlies	MAE, L1 error, mean absolute deviation	MSE, L2 error, quadratic error
Relacionats	3	4
Resum	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.