Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Matriki ya Kuchanganyikiwa× | Umahiri (Specificity)× | |
|---|---|---|
| Nyanja | Tathmini ya Modeli | Tathmini ya Modeli |
| Familia | MCDM | MCDM |
| Mwaka wa asili | 20th century | 20th century |
| Mwanzilishi≠ | Statistical foundations | Historical statistical foundations |
| Aina≠ | Evaluation visualization | Evaluation metric |
| Chanzo asilia≠ | Everitt, B. S., & Hothorn, T. (2005). A Handbook of Statistical Analyses Using R. Chapman and Hall/CRC. link ↗ | Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. DOI ↗ |
| Majina mbadala | Error Matrix, Contingency Table | True Negative Rate, TNR |
| Zinazohusiana | 5 | 5 |
| Muhtasari≠ | The confusion matrix is a table that displays the counts of true positives, true negatives, false positives, and false negatives. It provides a complete picture of where a classifier makes correct and incorrect predictions, enabling calculation of all other classification metrics. | 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. |
| ScholarGateSeti ya data ↗ |
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