Advanced · Evaluation
Hinge loss
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. A loss function that penalizes incorrect classifications and aims to maximize the margin around the decision boundary.
Technical Definition
A loss function that penalizes incorrect classifications and aims to maximize the margin around the decision boundary.
How it works
Hinge loss is a loss function commonly used in Support Vector Machines (SVMs) for binary classification. It penalizes predictions that are not only incorrect but also close to the decision boundary, aiming to create a large margin between classes. The loss is zero for correctly classified examples that are sufficiently far from the boundary.
Related Concepts
- Loss Function — A mathematical function that measures how far the model's predictions are from the actual values, guiding the learning process.
- Classification — A supervised learning task where the model assigns inputs to discrete categories.