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| Presisi× | Spesifisitas× | |
|---|---|---|
| Bidang | Evaluasi Model | Evaluasi Model |
| Keluarga | MCDM | MCDM |
| Tahun asal | 20th century | 20th century |
| Pencetus | Historical statistical foundations | Historical statistical foundations |
| Tipe | Evaluation metric | Evaluation metric |
| Sumber perintis | Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. DOI ↗ | Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. DOI ↗ |
| Alias | Positive Predictive Value, PPV | True Negative Rate, TNR |
| Terkait | 5 | 5 |
| Ringkasan≠ | Precision measures the proportion of positive predictions that were actually correct. It answers the question: 'Of all the cases we predicted as positive, how many were truly positive?' Precision is critical in scenarios where false positives are costly. | Specificity measures the proportion of actual negative cases that were correctly identified as negative by the classifier. It answers the question: 'Of all the cases that were truly negative, how many did we correctly reject?' Specificity is complementary to recall and is essential when false positives are costly. |
| ScholarGateSet data ↗ |
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