Comparar métodos

	Error Absoluto Medio (MAE)	Error Cuadrático Medio (RMSE)
Campo	Evaluación de modelos	Evaluación de modelos
Familia	MCDM	MCDM
Año de origen	1799	1809
Autor original	Pierre-Simon Laplace	Carl Friedrich Gauss
Tipo	Robust distance-based metric	Distance-based evaluation metric
Fuente seminal	Laplace, P. S. (1799). Traité de Mécanique Céleste. Paris: J.B.M. Duprat.	Gauss, C. F. (1809). Theoria Motus Corporum Coelestium in Sectionibus Conicis Solem Ambientium. Hamburg: Perthes and Besser.
Alias	MAE, L1 error, mean absolute deviation	RMSE, RMS error, quadratic mean error
Relacionados	3	4
Resumen	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.	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.