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| 均方误差 (MSE)× | 平均绝对误差 (MAE)× | |
|---|---|---|
| 领域 | 模型评估 | 模型评估 |
| 方法族 | MCDM | MCDM |
| 起源年份≠ | 1809 | 1799 |
| 提出者≠ | Carl Friedrich Gauss | Pierre-Simon Laplace |
| 类型≠ | Squared-error loss function | Robust distance-based metric |
| 开创性文献≠ | 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 ↗ |
| 别名 | MSE, L2 error, quadratic error | MAE, L1 error, mean absolute deviation |
| 相关≠ | 4 | 3 |
| 摘要≠ | 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. |
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