So sánh phương pháp

	Tiêu chí Thông tin Akaike (AIC)	Hệ số xác định (R²)
Lĩnh vực	Đánh giá mô hình	Đánh giá mô hình
Họ	MCDM	MCDM
Năm ra đời	1974	1896
Người khởi xướng	Hirotugu Akaike	Karl Pearson
Loại	Model selection metric	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.	Pearson, K. (1896). Mathematical contributions to the theory of evolution. Philosophical Transactions of the Royal Society A, 187, 253-318.
Tên gọi khác	AIC	R², coefficient of determination, r2 score
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.	The coefficient of determination, denoted R², measures the proportion of variance in the dependent variable explained by the independent variables in a regression model. Introduced by Karl Pearson in the late 19th century, R² is one of the most widely used metrics for assessing how well a model fits observed data.