Table 9.

Description of the performance evaluation metrics

Measures	Formula	Description
Accuracy	$\frac{TP+TN}{TP+FP+TN+FN}$	Accuracy refers to the amount of accurate assumptions the algorithm produces for forecasts of all sorts.
Precision	$\frac{TP}{TP+FP}$	Precision is the percentage of successful cases that were reported correctly.
Recall	$\frac{TP}{TP+FN}$	It is the number of right positive outcomes divided by the number of all related samples (including samples that were meant to be positive).
F1-score	$\frac{2\times P\times R}{P+R}$	It is the harmonic mean of the precision and recall values.
MCC	$\frac{(TP\ast TN-FP\ast FN)}{\sqrt{(TP+FP)\ast (TP+FN)\ast (TN+FP)\ast (TN+FN)}}$	MCC is the correlation coefficient between the actual values of the class and the predicted values of the class.
Specificity	$\frac{TN}{TP+FN}$	It is used to calculate the fraction of negative values correctly classified.
Gmean1	$\sqrt{Precision\times Recall}$	Gmean1 is computed as the square root of the product of precision and recall.
Gmean2	$\sqrt{Specificity\times Recall}$	Gmean2 is computed as the square root of the product of specificity and recall.