Salīdzināt metodes

	Vidējā kvadrātiskā kļūda (MSE)	R kvadrāts (R²)
Nozare	Modeļu novērtēšana	Modeļu novērtēšana
Saime	MCDM	MCDM
Izcelsmes gads	1809	1896
Autors	Carl Friedrich Gauss	Karl Pearson
Tips	Squared-error loss function	Goodness-of-fit metric
Pirmavots	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.
Citi nosaukumi	MSE, L2 error, quadratic error	R², coefficient of determination, r2 score
Saistītās	4	5
Kopsavilkums	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.