Sammenlign metoder
Gennemgå dine valgte metoder side om side; rækker, der afviger, er fremhævet.
| Middelfejl (MAE)× | Middelfejlskvadrat (MSE)× | |
|---|---|---|
| Fagområde | Modelevaluering | Modelevaluering |
| Familie | MCDM | MCDM |
| Oprindelsesår≠ | 1799 | 1809 |
| Ophavsperson≠ | Pierre-Simon Laplace | Carl Friedrich Gauss |
| Type≠ | Robust distance-based metric | Squared-error loss function |
| Oprindelig kilde≠ | 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 ↗ |
| Aliasser | MAE, L1 error, mean absolute deviation | MSE, L2 error, quadratic error |
| Relaterede≠ | 3 | 4 |
| Resumé≠ | 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. | Mean Squared Error is the foundational loss function for regression models, measuring the average squared deviation between predictions and observations. Originating from Gauss and Legendre's method of least squares (1805-1809), MSE is the basis for ordinary least squares regression and remains central to modern machine learning optimization. |
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