Comparer des méthodes

	Erreur Absolue Moyenne (EAM)	Erreur quadratique moyenne (EQM)
Domaine	Évaluation de modèles	Évaluation de modèles
Famille	MCDM	MCDM
Année d'origine	1799	1809
Auteur d'origine	Pierre-Simon Laplace	Carl Friedrich Gauss
Type	Robust distance-based metric	Squared-error loss function
Source fondatrice	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
Apparentées	3	4
Résumé	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.