So sánh phương pháp

	Tiêu chí Thông tin Akaike (AIC)	Hệ số xác định hiệu chỉnh (R²_adj)
Lĩnh vực	Đánh giá mô hình	Đánh giá mô hình
Họ	MCDM	MCDM
Năm ra đời	1974	1961
Người khởi xướng	Hirotugu Akaike	Henri Theil
Loại	Model selection metric	Penalized goodness-of-fit metric
Công trình gốc	Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716-723.	Theil, H. (1961). Economic Forecasts and Policy. Amsterdam: North-Holland Publishing Company.
Tên gọi khác	AIC	Adjusted R², R²_adj
Liên quan	4	5
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.	Adjusted R² is a corrected version of the coefficient of determination that accounts for the number of predictors in a regression model. Introduced by Henri Theil in 1961, it addresses the fundamental limitation of standard R²: the tendency to increase whenever any predictor is added, regardless of whether that predictor contributes meaningfully to explaining the target variable.