Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Root Mean Squared Error (RMSE)× | R-kwadraat (R²)× | |
|---|---|---|
| Vakgebied | Modelevaluatie | Modelevaluatie |
| Familie | MCDM | MCDM |
| Jaar van ontstaan≠ | 1809 | 1896 |
| Grondlegger≠ | Carl Friedrich Gauss | Karl Pearson |
| Type≠ | Distance-based evaluation metric | Goodness-of-fit metric |
| Oorspronkelijke bron≠ | 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 ↗ |
| Aliassen | RMSE, RMS error, quadratic mean error | R², coefficient of determination, r2 score |
| Verwant≠ | 4 | 5 |
| Samenvatting≠ | 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. |
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