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| 均方误差 (MSE)× | R平方 (R²)× | |
|---|---|---|
| 领域 | 模型评估 | 模型评估 |
| 方法族 | MCDM | MCDM |
| 起源年份≠ | 1809 | 1896 |
| 提出者≠ | Carl Friedrich Gauss | Karl Pearson |
| 类型≠ | Squared-error loss function | Goodness-of-fit metric |
| 开创性文献≠ | Gauss, C. F. (1809). Theoria Motus Corporum Coelestium in Sectionibus Conicis Solem Ambientium. Hamburg: Perthes and Besser. link ↗ | Pearson, K. (1896). Mathematical contributions to the theory of evolution. Philosophical Transactions of the Royal Society A, 187, 253-318. link ↗ |
| 别名 | MSE, L2 error, quadratic error | R², coefficient of determination, r2 score |
| 相关≠ | 4 | 5 |
| 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. | The coefficient of determination, denoted R², measures the proportion of variance in the dependent variable explained by the independent variables in a regression model. Introduced by Karl Pearson in the late 19th century, R² is one of the most widely used metrics for assessing how well a model fits observed data. |
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