So sánh phương pháp

	Sai số bình phương trung bình (MSE)	Sai số Tuyệt đối Trung bình (MAE)
Lĩnh vực	Đánh giá mô hình	Đánh giá mô hình
Họ	MCDM	MCDM
Năm ra đời	1809	1799
Người khởi xướng	Carl Friedrich Gauss	Pierre-Simon Laplace
Loại	Squared-error loss function	Robust distance-based metric
Công trình gốc	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.
Tên gọi khác	MSE, L2 error, quadratic error	MAE, L1 error, mean absolute deviation
Liên quan	4	3
Tóm tắt	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.