विधियों की तुलना करें

	माध्य वर्ग त्रुटि (MSE)	माध्य निरपेक्ष त्रुटि (MAE)
क्षेत्र	मॉडल मूल्यांकन	मॉडल मूल्यांकन
परिवार	MCDM	MCDM
उद्भव वर्ष	1809	1799
प्रवर्तक	Carl Friedrich Gauss	Pierre-Simon Laplace
प्रकार	Squared-error loss function	Robust distance-based metric
मौलिक स्रोत	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.
उपनाम	MSE, L2 error, quadratic error	MAE, L1 error, mean absolute deviation
संबंधित	4	3
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.	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.