পদ্ধতির তুলনা করুন

	সমন্বিত R-বর্গ (R²_adj)	গড় বর্গ ত্রুটি (MSE)
ক্ষেত্র	মডেল মূল্যায়ন	মডেল মূল্যায়ন
পরিবার	MCDM	MCDM
উদ্ভবের বছর	1961	1809
প্রবর্তক	Henri Theil	Carl Friedrich Gauss
ধরন	Penalized goodness-of-fit metric	Squared-error loss function
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	MSE, L2 error, quadratic 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.	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.