Table 2.

Performance metrics of the classifiers.

Performance metrics	Description of metrics	Derived from confusion matrix
Accuracy	Average of no of samples identified as positive to no. of samples identified as negative	$Ac=\frac{\left(TN+TP\right)}{\left(TN+FN+TP+FP\right)}$
Precision	From all correct predictions, accurately predicted.	$PR=\frac{TP}{\left(TP+FP\right)}$
MSE	Average of the squared error	$MSE=\frac{1}{N}\sum _{i=1}^N{\left(y_i-y_i^{\wedge }\right)}^2$
F1 score	Mean of precision and recall to get classification accuracy for a specific class	$F1=\frac{2\ast TP}{\left(2\ast TP+FP+FN\right)}$
Mathews correlation coefficient	Pearson correlation between true and attained output	$MCC\frac{\left(TP\ast TN-FP\ast FN\right)}{\sqrt{\left(\left(TP+FP\right)\ast \left(TN+FP\right)\ast \left(TN+FN\right)\right)}}$
Fowlkes mallows index	Measure of similarity between clustering	$FM=\sqrt{\frac{TP}{TP+FP}.\frac{TP}{TP+FN}}$
Error rate	Based on the number of observations, the sum of all inaccurate predictions.	$ErR=\frac{\left(FP+FN\right)}{\left(TP+FN+TN+FP\right)}$
Jaccard metric	The predicted real positives outnumbered the actual positives, whether they happened to be real or predicted.	$Jac=\frac{TP}{\left(TP+FP+FN\right)}$
Classification success index	Averaging the class-specific symmetric measure of overall class	CSI = PPV + SEN − 100