Compara mètodes

	Error Quadràtic Mitjà (MSE)	Coeficient de determinació (R²)
Camp	Avaluació de models	Avaluació de models
Família	MCDM	MCDM
Any d'origen	1809	1896
Autor original	Carl Friedrich Gauss	Karl Pearson
Tipus	Squared-error loss function	Goodness-of-fit metric
Font seminal	Gauss, C. F. (1809). Theoria Motus Corporum Coelestium in Sectionibus Conicis Solem Ambientium. Hamburg: Perthes and Besser.	Pearson, K. (1896). Mathematical contributions to the theory of evolution. Philosophical Transactions of the Royal Society A, 187, 253-318.
Àlies	MSE, L2 error, quadratic error	R², coefficient of determination, r2 score
Relacionats	4	5
Resum	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.	The coefficient of determination, denoted R², measures the proportion of variance in the dependent variable explained by the independent variables in a regression model. Introduced by Karl Pearson in the late 19th century, R² is one of the most widely used metrics for assessing how well a model fits observed data.