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| 조정된 결정계수 (Adjusted R² / R²_adj)× | 아카이케 정보량 기준 (AIC)× | |
|---|---|---|
| 분야 | 모델 평가 | 모델 평가 |
| 계열 | MCDM | MCDM |
| 기원 연도≠ | 1961 | 1974 |
| 창시자≠ | Henri Theil | Hirotugu Akaike |
| 유형≠ | Penalized goodness-of-fit metric | Model selection metric |
| 원전≠ | Theil, H. (1961). Economic Forecasts and Policy. Amsterdam: North-Holland Publishing Company. link ↗ | Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716-723. DOI ↗ |
| 별칭≠ | Adjusted R², R²_adj | AIC |
| 관련≠ | 5 | 4 |
| 요약≠ | 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. | 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. |
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