手法を比較

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