Bandingkan kaedah

	Ralat Kuasa Dua Min (MSE)	Ralat Mutlak Min (MAE)
Bidang	Penilaian Model	Penilaian Model
Keluarga	MCDM	MCDM
Tahun asal	1809	1799
Pengasas	Carl Friedrich Gauss	Pierre-Simon Laplace
Jenis	Squared-error loss function	Robust distance-based metric
Sumber perintis	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.
Alias	MSE, L2 error, quadratic error	MAE, L1 error, mean absolute deviation
Berkaitan	4	3
Ringkasan	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.