Salīdzināt metodes

	Koriģētais noteikšanas koeficients (R²_adj)	Vidējā kvadrātiskā kļūda (MSE)
Nozare	Modeļu novērtēšana	Modeļu novērtēšana
Saime	MCDM	MCDM
Izcelsmes gads	1961	1809
Autors	Henri Theil	Carl Friedrich Gauss
Tips	Penalized goodness-of-fit metric	Squared-error loss function
Pirmavots	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.
Citi nosaukumi	Adjusted R², R²_adj	MSE, L2 error, quadratic error
Saistītās	5	4
Kopsavilkums	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.