9. In deep learning, a sigmoid cross-entropy loss is preferred for multi-label classification because:

Question

9. In deep learning, a sigmoid cross-entropy loss is preferred for  multi-label classification  because:

Answer

It enforces exclusivity between labels

Answer

It treats each label as an independent binary task

Answer

It normalizes outputs across all classes

Answer

It penalizes only the most confident predictions

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