Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| R² ajustado (R²_adj)× | Criterio de Información de Akaike (AIC)× | |
|---|---|---|
| Campo | Evaluación de modelos | Evaluación de modelos |
| Familia | MCDM | MCDM |
| Año de origen≠ | 1961 | 1974 |
| Autor original≠ | Henri Theil | Hirotugu Akaike |
| Tipo≠ | Penalized goodness-of-fit metric | Model selection metric |
| Fuente seminal≠ | 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 ↗ |
| Alias≠ | Adjusted R², R²_adj | AIC |
| Relacionados≠ | 5 | 4 |
| Resumen≠ | 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. |
| ScholarGateConjunto de datos ↗ |
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