Linganisha mbinu

	Kigezo cha Taarifa cha Akaike (AIC)	R² iliyorekebishwa (R²_adj)
Nyanja	Tathmini ya Modeli	Tathmini ya Modeli
Familia	MCDM	MCDM
Mwaka wa asili	1974	1961
Mwanzilishi	Hirotugu Akaike	Henri Theil
Aina	Model selection metric	Penalized goodness-of-fit metric
Chanzo asilia	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.
Majina mbadala	AIC	Adjusted R², R²_adj
Zinazohusiana	4	5
Muhtasari	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.