手法を比較

	平均二乗誤差（MSE）	二乗平均平方根誤差 (RMSE)
分野	モデル評価	モデル評価
系統	MCDM	MCDM
提唱年	1809	1809
提唱者	Carl Friedrich Gauss	Carl Friedrich Gauss
種類	Squared-error loss function	Distance-based evaluation metric
原典	Gauss, C. F. (1809). Theoria Motus Corporum Coelestium in Sectionibus Conicis Solem Ambientium. Hamburg: Perthes and Besser.	Gauss, C. F. (1809). Theoria Motus Corporum Coelestium in Sectionibus Conicis Solem Ambientium. Hamburg: Perthes and Besser.
別名	MSE, L2 error, quadratic error	RMSE, RMS error, quadratic mean error
関連	4	4
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.	Root Mean Squared Error is a widely used metric that measures the average magnitude of prediction errors in regression models. Originating from Carl Friedrich Gauss's work on least-squares estimation (1809), RMSE quantifies how far predictions deviate from observed values by averaging the squared differences and taking the square root.