השוואת שיטות

	טעות ריבועית ממוצעת (MSE)	שגיאה מוחלטת ממוצעת (MAE)
תחום	הערכת מודלים	הערכת מודלים
משפחה	MCDM	MCDM
שנת המקור	1809	1799
הוגה השיטה	Carl Friedrich Gauss	Pierre-Simon Laplace
סוג	Squared-error loss function	Robust distance-based metric
מקור מכונן	Gauss, C. F. (1809). Theoria Motus Corporum Coelestium in Sectionibus Conicis Solem Ambientium. Hamburg: Perthes and Besser.	Laplace, P. S. (1799). Traité de Mécanique Céleste. Paris: J.B.M. Duprat.
כינויים	MSE, L2 error, quadratic error	MAE, L1 error, mean absolute deviation
קשורות	4	3
תקציר	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.	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.