Key characteristics of multi-label datasets

1. Multiple Labels per Instance: Each instance in the dataset can have one or more associated labels, rather than just a single label.
2. Dependent Labels: The labels in a multi-label dataset can be dependent on each other, meaning that the presence of one label may be related to the presence of another.
3. Imbalanced Labels: The distribution of labels in a multi-label dataset is often imbalanced, with some labels being much more common than others.
4. Computational Complexity: Handling multi-label datasets can be computationally more complex than single-label datasets, as the model needs to learn to predict multiple labels simultaneously.