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| Errore Assoluto Medio (MAE)× | Errore quadratico medio (RMSE)× | |
|---|---|---|
| Campo | Valutazione dei modelli | Valutazione dei modelli |
| Famiglia | MCDM | MCDM |
| Anno di origine≠ | 1799 | 1809 |
| Ideatore≠ | Pierre-Simon Laplace | Carl Friedrich Gauss |
| Tipo≠ | Robust distance-based metric | Distance-based evaluation metric |
| Fonte seminale≠ | Laplace, P. S. (1799). Traité de Mécanique Céleste. Paris: J.B.M. Duprat. link ↗ | Gauss, C. F. (1809). Theoria Motus Corporum Coelestium in Sectionibus Conicis Solem Ambientium. Hamburg: Perthes and Besser. link ↗ |
| Alias | MAE, L1 error, mean absolute deviation | RMSE, RMS error, quadratic mean error |
| Correlati≠ | 3 | 4 |
| Sintesi≠ | Mean Absolute Error is a robust metric that measures the average absolute magnitude of prediction errors in regression models. Dating back to Pierre-Simon Laplace's work on observational errors (1799), MAE quantifies typical prediction deviation by averaging the absolute differences between observed and predicted values. | 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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