手法を比較

	平均絶対誤差 (MAE)	平均二乗誤差（MSE）
分野	モデル評価	モデル評価
系統	MCDM	MCDM
提唱年	1799	1809
提唱者	Pierre-Simon Laplace	Carl Friedrich Gauss
種類	Robust distance-based metric	Squared-error loss function
原典	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.
別名	MAE, L1 error, mean absolute deviation	MSE, L2 error, quadratic error
関連	3	4
概要	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.