Table 1 The hypothetical formulation of the model’s evaluation metrics based on the classifier outputs and sample true label
Measure | Mathematical formula |
|---|---|
Accuracy | \(\text {Accuracy} = \frac{TP + TN}{\text {Total samples}}\) |
Precision | \(\text {Precision} = \frac{TP}{TP + FP}\) |
Recall (sensitivity) | \(\text {Recall} = \frac{TP}{TP + FN}\) |
F1-score | \(\text {F1-score} = \frac{2 \times Precision \times recall}{Precision + Recall}\) |
Kappa | \(\text {Kappa} = \frac{\text {Accuracy} - \text {Expected accuracy}}{1 - \text {Expected accuracy}}\) |
MCC | \(\text {MCC} = \frac{TP \times TN - FP \times FN}{\sqrt{(TP + FP) \times (TP + FN) \times (TN + FP) \times (TN + FN)}}\) |
Zero-one loss | \(\text {Zero-one L} = \frac{\text {Number of Misclassified samples}}{\text {Total samples}}\) |
Hamming loss | \(\text {HL} = \frac{\text {Number of incorrect label assignments}}{\text {Total number of labels}}\) |