Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Симметричная MAPE (sMAPE)× | Среднеквадратичная ошибка (RMSE)× | |
|---|---|---|
| Область | Оценка моделей | Оценка моделей |
| Семейство | MCDM | MCDM |
| Год появления≠ | 1985 | 1809 |
| Автор метода≠ | J. Scott Armstrong | Carl Friedrich Gauss |
| Тип≠ | Symmetric percentage-based evaluation metric | Distance-based evaluation metric |
| Основополагающий источник≠ | Armstrong, J. S. (1985). Long-range forecasting: from crystal ball to computer (2nd ed.). New York: John Wiley & Sons. ISBN: 978-0471082010 | Gauss, C. F. (1809). Theoria Motus Corporum Coelestium in Sectionibus Conicis Solem Ambientium. Hamburg: Perthes and Besser. link ↗ |
| Другие названия | sMAPE, SMAPE, symmetric MAPE | RMSE, RMS error, quadratic mean error |
| Связанные | 4 | 4 |
| Сводка≠ | Symmetric Mean Absolute Percentage Error is a refinement of MAPE that addresses its asymmetry by using the average of actual and predicted values as the denominator. Proposed by J. Scott Armstrong and refined by Makridakis (1993) and Hyndman & Koehler (2006), sMAPE treats over- and under-predictions symmetrically. | 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. |
| ScholarGateНабор данных ↗ |
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