विधियों की तुलना करें

	माध्य निरपेक्ष त्रुटि (MAE)	मूल माध्य वर्ग त्रुटि (Root Mean Squared Error - RMSE)
क्षेत्र	मॉडल मूल्यांकन	मॉडल मूल्यांकन
परिवार	MCDM	MCDM
उद्भव वर्ष	1799	1809
प्रवर्तक	Pierre-Simon Laplace	Carl Friedrich Gauss
प्रकार	Robust distance-based metric	Distance-based evaluation metric
मौलिक स्रोत	Laplace, P. S. (1799). Traité de Mécanique Céleste. Paris: J.B.M. Duprat.	Gauss, C. F. (1809). Theoria Motus Corporum Coelestium in Sectionibus Conicis Solem Ambientium. Hamburg: Perthes and Besser.
उपनाम	MAE, L1 error, mean absolute deviation	RMSE, RMS error, quadratic mean error
संबंधित	3	4
सारांश	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.	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.