השוואת שיטות

	קריטריון המידע של אקאיקה (AIC)	מקדם המתאם המתוקנן (R²_adj)
תחום	הערכת מודלים	הערכת מודלים
משפחה	MCDM	MCDM
שנת המקור	1974	1961
הוגה השיטה	Hirotugu Akaike	Henri Theil
סוג	Model selection metric	Penalized goodness-of-fit metric
מקור מכונן	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.
כינויים	AIC	Adjusted R², R²_adj
קשורות	4	5
תקציר	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.