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Table 2.
Performance metrics used for model evaluation (Sokolova & Lapalme 2009).
| Metric | Definition |
|---|---|
| Accuracy | =(TP + TN)/(TP + TN + FP + FN) |
| Ratio of correct predictions to the total number | |
| of predictions. | |
| Precision | =TP/(TP + FP) |
| High precision indicates a low number of false | |
| positives. This term is also called the purity. | |
| Recall | =TP/(TP + FN) |
| High recall indicates a low number of false | |
| negatives. This term is also called the completeness. | |
| F1-score | Harmonic mean of precision and recall. |
| Provides a balanced evaluation of both metrics. | |
| Support | Number of true instances for each class. |
Notes. TP = true positives, TN = true negatives, FP = false positives, and FN = false negatives.
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