seniorSupervised Learning
What is the difference between hinge loss and cross-entropy loss?
Updated May 17, 2026
Short answer
Hinge loss is used in SVMs for margin maximization, while cross-entropy is used in probabilistic classification.
Deep explanation
Hinge loss penalizes predictions based on margin violations and is used in max-margin classifiers like SVM. Cross-entropy loss measures difference between predicted probability distribution and true labels. Cross-entropy is smoother and widely used in neural networks.
Real-world example
SVM uses hinge loss for text classification; neural networks use cross-entropy for image classification.
Common mistakes
- Using hinge loss when probabilistic outputs are required.
Follow-up questions
- Why is cross-entropy preferred in deep learning?
- What is margin in hinge loss?