Сравнение методов

	Среднеквадратичная ошибка (MSE)	Среднеквадратичная ошибка (RMSE)
Область	Оценка моделей	Оценка моделей
Семейство	MCDM	MCDM
Год появления	1809	1809
Автор метода	Carl Friedrich Gauss	Carl Friedrich Gauss
Тип	Squared-error loss function	Distance-based evaluation metric
Основополагающий источник	Gauss, C. F. (1809). Theoria Motus Corporum Coelestium in Sectionibus Conicis Solem Ambientium. Hamburg: Perthes and Besser.	Gauss, C. F. (1809). Theoria Motus Corporum Coelestium in Sectionibus Conicis Solem Ambientium. Hamburg: Perthes and Besser.
Другие названия	MSE, L2 error, quadratic error	RMSE, RMS error, quadratic mean error
Связанные	4	4
Сводка	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.	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.