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| Analyse ROC (Courbe Caractéristique d'Opération du Récepteur)× | Analyse discriminante× | |
|---|---|---|
| Domaine | Statistique | Statistique |
| Famille≠ | Hypothesis test | Latent structure |
| Année d'origine≠ | 1954 (signal detection); 1982 (AUC formalization) | 1936 |
| Auteur d'origine≠ | Peterson, Birdsall & Fox (signal detection theory); Hanley & McNeil (medical statistics) | Ronald A. Fisher |
| Type≠ | Diagnostic accuracy evaluation | Supervised classification and dimension reduction |
| Source fondatrice≠ | 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 ↗ |
| Alias | ROC curve analysis, AUC analysis, sensitivity-specificity analysis, diagnostic accuracy analysis | LDA, Fisher discriminant analysis, discriminant function analysis, canonical discriminant analysis |
| Apparentées | 4 | 4 |
| Résumé≠ | 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. |
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