Salīdzināt metodes

	Vidējā kvadrātiskā kļūda (MSE)	Vidējā absolūtā kļūda (MAE)
Nozare	Modeļu novērtēšana	Modeļu novērtēšana
Saime	MCDM	MCDM
Izcelsmes gads	1809	1799
Autors	Carl Friedrich Gauss	Pierre-Simon Laplace
Tips	Squared-error loss function	Robust distance-based metric
Pirmavots	Gauss, C. F. (1809). Theoria Motus Corporum Coelestium in Sectionibus Conicis Solem Ambientium. Hamburg: Perthes and Besser.	Laplace, P. S. (1799). Traité de Mécanique Céleste. Paris: J.B.M. Duprat.
Citi nosaukumi	MSE, L2 error, quadratic error	MAE, L1 error, mean absolute deviation
Saistītās	4	3
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.	Mean Absolute Error is a robust metric that measures the average absolute magnitude of prediction errors in regression models. Dating back to Pierre-Simon Laplace's work on observational errors (1799), MAE quantifies typical prediction deviation by averaging the absolute differences between observed and predicted values.