Methoden vergelijken

	Gemiddelde Absolute Fout (MAE)	Gemiddelde Kwadratische Fout (MSE)
Vakgebied	Modelevaluatie	Modelevaluatie
Familie	MCDM	MCDM
Jaar van ontstaan	1799	1809
Grondlegger	Pierre-Simon Laplace	Carl Friedrich Gauss
Type	Robust distance-based metric	Squared-error loss function
Oorspronkelijke bron	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.
Aliassen	MAE, L1 error, mean absolute deviation	MSE, L2 error, quadratic error
Verwant	3	4
Samenvatting	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.