midSVM
What is hinge loss in SVM?
Updated May 17, 2026
Short answer
Hinge loss penalizes misclassified points and points within the margin.
Deep explanation
SVM optimizes hinge loss which ensures correct classification with a margin. Loss increases when points are on wrong side or too close to boundary.
Real-world example
Used in text classification models like spam filters.
Common mistakes
- Confusing hinge loss with logistic loss.
Follow-up questions
- Why not use squared error?
- Is hinge loss convex?