方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| R平方 (R²)× | 均方根误差 (RMSE)× | |
|---|---|---|
| 领域 | 模型评估 | 模型评估 |
| 方法族 | MCDM | MCDM |
| 起源年份≠ | 1896 | 1809 |
| 提出者≠ | Karl Pearson | Carl Friedrich Gauss |
| 类型≠ | Goodness-of-fit metric | Distance-based evaluation metric |
| 开创性文献≠ | Pearson, K. (1896). Mathematical contributions to the theory of evolution. Philosophical Transactions of the Royal Society A, 187, 253-318. link ↗ | Gauss, C. F. (1809). Theoria Motus Corporum Coelestium in Sectionibus Conicis Solem Ambientium. Hamburg: Perthes and Besser. link ↗ |
| 别名 | R², coefficient of determination, r2 score | RMSE, RMS error, quadratic mean error |
| 相关≠ | 5 | 4 |
| 摘要≠ | 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. | Root Mean Squared Error is a widely used metric that measures the average magnitude of prediction errors in regression models. Originating from Carl Friedrich Gauss's work on least-squares estimation (1809), RMSE quantifies how far predictions deviate from observed values by averaging the squared differences and taking the square root. |
| ScholarGate数据集 ↗ |
|
|