So sánh phương pháp

	Tiêu chí Thông tin Akaike (AIC)	Sai số bình phương trung bình (MSE)
Lĩnh vực	Đánh giá mô hình	Đánh giá mô hình
Họ	MCDM	MCDM
Năm ra đời	1974	1809
Người khởi xướng	Hirotugu Akaike	Carl Friedrich Gauss
Loại	Model selection metric	Squared-error loss function
Công trình gốc	Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716-723.	Gauss, C. F. (1809). Theoria Motus Corporum Coelestium in Sectionibus Conicis Solem Ambientium. Hamburg: Perthes and Besser.
Tên gọi khác	AIC	MSE, L2 error, quadratic error
Liên quan	4	4
Tóm tắt	The Akaike Information Criterion is an information-theoretic measure for model selection that balances goodness of fit against model complexity. Introduced by Hirotugu Akaike in 1974, AIC estimates the relative quality of models for a given dataset, penalizing additional parameters to prevent overfitting.	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.