विधियों की तुलना करें

	माध्य निरपेक्ष मापित त्रुटि (MASE)	मूल माध्य वर्ग त्रुटि (Root Mean Squared Error - RMSE)
क्षेत्र	मॉडल मूल्यांकन	मॉडल मूल्यांकन
परिवार	MCDM	MCDM
उद्भव वर्ष	2006	1809
प्रवर्तक	Rob J. Hyndman and Anne B. Koehler	Carl Friedrich Gauss
प्रकार	Scale-independent baseline comparison metric	Distance-based evaluation metric
मौलिक स्रोत	Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679-688.	Gauss, C. F. (1809). Theoria Motus Corporum Coelestium in Sectionibus Conicis Solem Ambientium. Hamburg: Perthes and Besser.
उपनाम	MASE	RMSE, RMS error, quadratic mean error
संबंधित	4	4
सारांश	Mean Absolute Scaled Error is a scale-independent metric that measures prediction accuracy relative to a simple baseline (naive forecast). Introduced by Hyndman and Koehler (2006), MASE directly compares model performance to a reference method, overcoming limitations of MAPE and other percentage-based metrics.	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.