방법 비교

	조정된 결정계수 (Adjusted R² / R²_adj)	평균 제곱근 오차 (Root Mean Squared Error, RMSE)
분야	모델 평가	모델 평가
계열	MCDM	MCDM
기원 연도	1961	1809
창시자	Henri Theil	Carl Friedrich Gauss
유형	Penalized goodness-of-fit metric	Distance-based evaluation metric
원전	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.
별칭	Adjusted R², R²_adj	RMSE, RMS error, quadratic mean error
관련	5	4
요약	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.