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| Adjusted R-squared (R²_adj)× | Wurzel der Mittleren Quadratischen Fehler (RMSE)× | |
|---|---|---|
| Fachgebiet | Modellevaluation | Modellevaluation |
| Familie | MCDM | MCDM |
| Entstehungsjahr≠ | 1961 | 1809 |
| Urheber≠ | Henri Theil | Carl Friedrich Gauss |
| Typ≠ | Penalized goodness-of-fit metric | Distance-based evaluation metric |
| Wegweisende Quelle≠ | Theil, H. (1961). Economic Forecasts and Policy. Amsterdam: North-Holland Publishing Company. link ↗ | Gauss, C. F. (1809). Theoria Motus Corporum Coelestium in Sectionibus Conicis Solem Ambientium. Hamburg: Perthes and Besser. link ↗ |
| Aliasnamen≠ | Adjusted R², R²_adj | RMSE, RMS error, quadratic mean error |
| Verwandt≠ | 5 | 4 |
| Zusammenfassung≠ | 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. | Root Mean Squared Error is a widely used metric that measures the average magnitude of prediction errors in regression models. Originating from Carl Friedrich Gauss's work on least-squares estimation (1809), RMSE quantifies how far predictions deviate from observed values by averaging the squared differences and taking the square root. |
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