Reference It ranges from 0 to 1, where: 0-no correct ratings 1-perfect classification [54] Where, ACCoverall: relative compliance observed between classifiers; RACCoverall: hypothetical probability compliance using the observed **data** to calculate the probabilities of each classifier to identify each category randomly; TP: true positive; TN: true negative; FP: false positive; FN: false negative; PPV: positive predictive value; TPR: true positive rate; NPV: negative predictive value; TNR: true negative rate.
Mathews Correlation Coefficient MCC = TP × TN − FP × FN √(
The adjusted F-score is an index that groups all elements of the original confusion matrix and gives more weight to correctly classified patterns in correctly classified classes [52]. The Matthews Correlation Coefficient has a range from −1 to 1, and when it has a value of −1, it indicates a completely wrong binary classifier, while 1 indicates a completely correct binary classifier. This index allows you to evaluate the performance of your classification model [53], whereas similarity is a metric ranging from 0 (wrong classification) to 1 (perfect classification) calculated from the averages of the classifications of the classes of interest [54].