Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Анализ ROC (Receiver Operating Characteristic)× | Дискриминантный анализ× | |
|---|---|---|
| Область | Статистика | Статистика |
| Семейство≠ | Hypothesis test | Latent structure |
| Год появления≠ | 1954 (signal detection); 1982 (AUC formalization) | 1936 |
| Автор метода≠ | Peterson, Birdsall & Fox (signal detection theory); Hanley & McNeil (medical statistics) | Ronald A. Fisher |
| Тип≠ | Diagnostic accuracy evaluation | Supervised classification and dimension reduction |
| Основополагающий источник≠ | Hanley, J. A., & McNeil, B. J. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143(1), 29–36. DOI ↗ | Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188. DOI ↗ |
| Другие названия | ROC curve analysis, AUC analysis, sensitivity-specificity analysis, diagnostic accuracy analysis | LDA, Fisher discriminant analysis, discriminant function analysis, canonical discriminant analysis |
| Связанные | 4 | 4 |
| Сводка≠ | ROC analysis evaluates how well a continuous or ordinal test variable discriminates between two binary outcome classes. By plotting the true positive rate (sensitivity) against the false positive rate (1 − specificity) across all decision thresholds, it produces a curve whose area under the curve (AUC) quantifies overall discriminative power, ranging from 0.5 (chance) to 1.0 (perfect discrimination). | Discriminant analysis finds linear combinations of predictor variables that best separate two or more known groups. It is used both to understand which predictors distinguish the groups and to classify new observations into those groups with minimum error. |
| ScholarGateНабор данных ↗ |
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