Table 5.

Classification performance metrics

Metric	Explanation	Formula
Accuracy	How often are the model's predictions correct?	(TP + TN) / (TP + TN + FN + FP)
Precision	When the model predicts positive, how often is it correct?	TP / (TP + FP)
Sensitivity (Recall) or TPR	When it is actually positive, how often it correctly predicts?	TP / (TP + FN)
Specificity or TNR	When it is actually negative, how often it correctly predicts?	TN / (TN + FP)
Balanced accuracy	The mean of sensitivity and specificity	(TPR + TNR) / 2
F-Measure	The harmonic mean of precision and sensitivity	2 × Precision × Recall / (Precision + Recall)