পদ্ধতির তুলনা করুন

	গড় বর্গ ত্রুটি (MSE)	Root Mean Squared Error (RMSE) (মূল গড় বর্গমূল ত্রুটি)
ক্ষেত্র	মডেল মূল্যায়ন	মডেল মূল্যায়ন
পরিবার	MCDM	MCDM
উদ্ভবের বছর	1809	1809
প্রবর্তক	Carl Friedrich Gauss	Carl Friedrich Gauss
ধরন	Squared-error loss function	Distance-based evaluation metric
মৌলিক উৎস	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.
অপর নাম	MSE, L2 error, quadratic error	RMSE, RMS error, quadratic mean error
সম্পর্কিত	4	4
সারসংক্ষেপ	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.