Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Анализ на ROC (Receiver Operating Characteristic)× | Дискриминантен анализ× | |
|---|---|---|
| Област | Статистика | Статистика |
| Семейство≠ | Hypothesis test | Latent structure |
| Година на възникване≠ | 1954 (signal detection); 1982 (AUC formalization) | 1936 |
| Създател≠ | Peterson, Birdsall & Fox (signal detection theory); Hanley & McNeil (medical statistics) | Ronald A. Fisher |
| Тип≠ | Diagnostic accuracy evaluation | Supervised classification and dimension reduction |
| Основополагащ източник≠ | 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 ↗ |
| Други названия | ROC curve analysis, AUC analysis, sensitivity-specificity analysis, diagnostic accuracy analysis | LDA, Fisher discriminant analysis, discriminant function analysis, canonical discriminant analysis |
| Свързани | 4 | 4 |
| Резюме≠ | 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 от данни ↗ |
|
|