विधियों की तुलना करें

	माध्य वर्ग त्रुटि (MSE)	मूल माध्य वर्ग त्रुटि (Root Mean Squared Error - RMSE)
क्षेत्र	मॉडल मूल्यांकन	मॉडल मूल्यांकन
परिवार	MCDM	MCDM
उद्भव वर्ष	1809	1809
प्रवर्तक	Carl Friedrich Gauss	Carl Friedrich Gauss
प्रकार	Squared-error loss function	Distance-based evaluation metric
मौलिक स्रोत	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.
उपनाम	MSE, L2 error, quadratic error	RMSE, RMS error, quadratic mean error
संबंधित	4	4
सारांश	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.