Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Medelkvadratfel (MSE)× | Medelabsolutfelet (MAE)× | |
|---|---|---|
| Ämnesområde | Modellutvärdering | Modellutvärdering |
| Familj | MCDM | MCDM |
| Ursprungsår≠ | 1809 | 1799 |
| Upphovsperson≠ | Carl Friedrich Gauss | Pierre-Simon Laplace |
| Typ≠ | Squared-error loss function | Robust distance-based metric |
| Ursprungskälla≠ | Gauss, C. F. (1809). Theoria Motus Corporum Coelestium in Sectionibus Conicis Solem Ambientium. Hamburg: Perthes and Besser. link ↗ | Laplace, P. S. (1799). Traité de Mécanique Céleste. Paris: J.B.M. Duprat. link ↗ |
| Alias | MSE, L2 error, quadratic error | MAE, L1 error, mean absolute deviation |
| Närliggande≠ | 4 | 3 |
| Sammanfattning≠ | 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. | 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. |
| ScholarGateDatamängd ↗ |
|
|