- Multiple Labels per Instance: Each instance in the dataset can have one or more associated labels, rather than just a single label.
- 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.
- Imbalanced Labels: The distribution of labels in a multi-label dataset is often imbalanced, with some labels being much more common than others.
- 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.
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Saturday, April 13, 2024
Key characteristics of multi-label datasets
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