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| Średni błąd kwadratowy (MSE)× | Współczynnik determinacji (R²)× | |
|---|---|---|
| Dziedzina | Ocena modeli | Ocena modeli |
| Rodzina | MCDM | MCDM |
| Rok powstania≠ | 1809 | 1896 |
| Twórca≠ | Carl Friedrich Gauss | Karl Pearson |
| Typ≠ | Squared-error loss function | Goodness-of-fit metric |
| Źródło pierwotne≠ | 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 ↗ |
| Inne nazwy | MSE, L2 error, quadratic error | R², coefficient of determination, r2 score |
| Pokrewne≠ | 4 | 5 |
| Podsumowanie≠ | 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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