Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Error Cuadrático Medio (RMSE)× | R-cuadrado (R²)× | |
|---|---|---|
| Campo | Evaluación de modelos | Evaluación de modelos |
| Familia | MCDM | MCDM |
| Año de origen≠ | 1809 | 1896 |
| Autor original≠ | Carl Friedrich Gauss | Karl Pearson |
| Tipo≠ | Distance-based evaluation metric | Goodness-of-fit metric |
| Fuente seminal≠ | 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 ↗ |
| Alias | RMSE, RMS error, quadratic mean error | R², coefficient of determination, r2 score |
| Relacionados≠ | 4 | 5 |
| Resumen≠ | 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. | 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. |
| ScholarGateConjunto de datos ↗ |
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