विधियों की तुलना करें

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