方法对比

	调整R方 (R²_adj)	均方根误差 (RMSE)
领域	模型评估	模型评估
方法族	MCDM	MCDM
起源年份	1961	1809
提出者	Henri Theil	Carl Friedrich Gauss
类型	Penalized goodness-of-fit metric	Distance-based evaluation metric
开创性文献	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.
别名	Adjusted R², R²_adj	RMSE, RMS error, quadratic mean error
相关	5	4
摘要	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.