Train the model in Multilabel mode

In Multilabel mode, the model is trained to classify each input into multiple categories simultaneously, meaning that each instance can belong to more than one class or label.

This differs from standard single-label classification where an input is assigned only one label.

In multilabel classification, the model is designed to handle the complexity of predicting several relevant labels for the same input.