Q6. Cross-Entropy (MCQ)

Why is cross-entropy (with softmax outputs) such a popular loss for multi-class classification?

• It maximizes entropy in the predictions, spreading probability evenly across classes.
• It exactly matches the 0-1 loss at test time.
• It allows gradient-based training and strongly penalizes assigning low probability to the correct class, aligning with the likelihood principle. <- correct
• It only requires logits and cannot be used with probabilities.