Matthews' Similarity and Correlation are classification metrics considered to be less biased as they use multiple input variables; they incorporate both the set imbalance and the amount of **data** referring to classes [34]. In this regard, the inclusion of species caused lower values of those metrics in comparison to Accuracy and the F-score since they have bias related to the number of characteristics, and, specifically, the F-score does not depend on the true negatives (TN). The high similarity observed here demonstrates that the classifier can identify true negatives, just as the high sensitivity indicates that the classifier was able to identify true positives [35].