AI engineering glossary · Classical ML algorithms
What is hinge loss?
Hinge loss: A loss function for margin classifiers: max(0, 1 - y * f(x)), where y is +1 or -1 and f(x) is the raw score. It is zero for points correctly classified beyond the margin and grows linearly for points inside it or misclassified.
Hinge loss: an example
A positive example (y=+1) with score 0.3 has loss 0.7; with score 1.5 it has loss 0.
Related terms
- Support vector machine
- Soft margin
- Cross-entropy loss
- Loss function
More Classical ML algorithms terms
Where you learn it
Hinge loss is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.