Table 3.

Metrics used to evaluate the performance of machine learned models

Metric Name/Notation	Description
Area under curve (AUC)	The area under curve for the receiver operating characteristics curve for different values of true positive rates and false positive rates.
Sensitivity	$\frac{TP}{TP+FN}$
Specificity	$\frac{TN}{TN+FP}$
Precision	$\frac{TP}{TP+FP}$
False negative rate (FNR)	$\frac{FN}{FN+TP}$
False positive rate (FPR)	$\frac{FP}{FP+TN}$
Accuracy	$\frac{TP+TN}{TP+TN+FP+FN}$
F1 score	$\frac{2TP}{2TP+FP+FN}$
Matthews correlation coefficient	$\frac{TP\ast TN-FP\ast FN}{\sqrt{(TP+FP)(TP+FN)(TN+FP)(TN+FN)}}$

TP: True positive, FP: False positive, TN: True negative, FN: False negative