השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| ניתוח ROC (Receiver Operating Characteristic)× | ניתוח מבחין× | |
|---|---|---|
| תחום | סטטיסטיקה | סטטיסטיקה |
| משפחה≠ | Hypothesis test | Latent structure |
| שנת המקור≠ | 1954 (signal detection); 1982 (AUC formalization) | 1936 |
| הוגה השיטה≠ | Peterson, Birdsall & Fox (signal detection theory); Hanley & McNeil (medical statistics) | Ronald A. Fisher |
| סוג≠ | Diagnostic accuracy evaluation | Supervised classification and dimension reduction |
| מקור מכונן≠ | 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 ↗ |
| כינויים | ROC curve analysis, AUC analysis, sensitivity-specificity analysis, diagnostic accuracy analysis | LDA, Fisher discriminant analysis, discriminant function analysis, canonical discriminant analysis |
| קשורות | 4 | 4 |
| תקציר≠ | 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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