Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Erro Médio Absoluto (MAE)× | Erro Quadrático Médio (EQM)× | |
|---|---|---|
| Área | Avaliação de modelos | Avaliação de modelos |
| Família | MCDM | MCDM |
| Ano de origem≠ | 1799 | 1809 |
| Autor original≠ | Pierre-Simon Laplace | Carl Friedrich Gauss |
| Tipo≠ | Robust distance-based metric | Squared-error loss function |
| Fonte seminal≠ | 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 ↗ |
| Outros nomes | MAE, L1 error, mean absolute deviation | MSE, L2 error, quadratic error |
| Relacionados≠ | 3 | 4 |
| Resumo≠ | 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. |
| ScholarGateConjunto de dados ↗ |
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