Confusion matrix

	Positive (P)	Negative (N)
Predicted positive (PP)	True positive (TP)	False positive (FP, Type I error)
Predicted negative (PN)	False negative (FN, Type II error)	True negative (TN)

$$precision = \frac{TP}{PP} = \frac{TP}{TP + FP}$$ $$recall = \frac{TP}{P} = \frac{TP}{TP + FN}$$ $$accuracy = \frac{TP + TN}{P + N}$$ $$specificity = \frac{TN}{N} = \frac{TP}{FP + TN}$$ $$threatScore = \frac{TP}{TP+FP+FN}$$

Threat score structurally reminds Jaccard index

$$F_1 = \frac{2TP}{2TP+FP+FN}$$

$F_1$ score structurally reminds Li, Bo (normalisation)