Regarding binary classification, we typically measure imbalance based on just two classes: the majority and minority classes. However, multi-label datasets often contain hundreds of labels, some with very low or high frequencies. Hence, it's crucial to assess the imbalance in multi-label classification (MLC) across all labels, not just two. To address this, we use the following metrics, as detailed by Charte et al. .