Each data point in a multi-label dataset is often linked to several labels. The likelihood that some labels are uncommon or frequent increases with the number of labels. Nonetheless, inferring the imbalance level from metrics like Card and Dens-the most frequently used in the literature to describe multi-label datasets-is not straightforward. Each sample of this multi-label dataset is related with somewhat more than three labels on average, according to Table 2's 3.102 Card value of IGD. Therefore, in a single occurrence, any of its 109 labels may appear with two or three other labels. Although neither Card nor Dens are designed to take this into account, this includes pairings in which the labels occur most and least frequently together. A graphical depiction alone is not a reliable way to assess the degree of imbalance . As a result, precise measurements are required to determine the degree of imbalance in multi-label datasets.