পদ্ধতির তুলনা করুন

	সমন্বিত R-বর্গ (R²_adj)	Root Mean Squared Error (RMSE) (মূল গড় বর্গমূল ত্রুটি)
ক্ষেত্র	মডেল মূল্যায়ন	মডেল মূল্যায়ন
পরিবার	MCDM	MCDM
উদ্ভবের বছর	1961	1809
প্রবর্তক	Henri Theil	Carl Friedrich Gauss
ধরন	Penalized goodness-of-fit metric	Distance-based evaluation metric
মৌলিক উৎস	Theil, H. (1961). Economic Forecasts and Policy. Amsterdam: North-Holland Publishing Company.	Gauss, C. F. (1809). Theoria Motus Corporum Coelestium in Sectionibus Conicis Solem Ambientium. Hamburg: Perthes and Besser.
অপর নাম	Adjusted R², R²_adj	RMSE, RMS error, quadratic mean error
সম্পর্কিত	5	4
সারসংক্ষেপ	Adjusted R² is a corrected version of the coefficient of determination that accounts for the number of predictors in a regression model. Introduced by Henri Theil in 1961, it addresses the fundamental limitation of standard R²: the tendency to increase whenever any predictor is added, regardless of whether that predictor contributes meaningfully to explaining the target variable.	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.