Сравнение методов

	Тестирование на соответствие (Goodness-of-Fit Testing)	Среднеквадратичная ошибка (MSE)
Область	Оценка моделей	Оценка моделей
Семейство	MCDM	MCDM
Год появления	1900	1809
Автор метода	Karl Pearson	Carl Friedrich Gauss
Тип	Hypothesis testing framework for model adequacy	Squared-error loss function
Основополагающий источник	Pearson, K. (1900). On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Philosophical Magazine, 50(302), 157-175.	Gauss, C. F. (1809). Theoria Motus Corporum Coelestium in Sectionibus Conicis Solem Ambientium. Hamburg: Perthes and Besser.
Другие названия	goodness of fit test, GOF test, model fit assessment	MSE, L2 error, quadratic error
Связанные	4	4
Сводка	Goodness-of-fit (GOF) testing is a framework for assessing whether observed data are consistent with a hypothesized probability distribution or model. Originating from Karl Pearson's chi-square test (1900), GOF tests quantify the discrepancy between data and model predictions, yielding p-values to judge whether observed deviations are statistically significant or due to random chance.	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.