A loss function for maximum-margin classification, used in SVMs.
This concept is essential for understanding model evaluation & metrics and forms a key part of modern AI systems.
Related Concepts
- Loss Function
- SVM
- Margin
Standard 7 stops to get here
Hinge Loss
A loss function for maximum-margin classification, used in SVMs.
Your route here
7 stops · basics first
- Dataset ✓ understood
A collection of data examples used for training, validating, or testing machine learning models.
- Training ✓ understood
The process of fitting a model to data by repeatedly measuring how wrong its outputs are and adjusting its parameters to reduce that error.
- Loss Function ✓ understood
A function that scores how wrong a model's prediction is as a single number, which training then works to make as small as possible.
- Machine Learning ✓ understood
Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.
- Supervised Learning ✓ understood
Learning from examples paired with the correct answer, so a model can predict answers for new inputs it hasn't seen.
- Classification ✓ understood
A supervised learning task where the model assigns each input to one of a fixed set of categories, such as spam or not spam.
- Support Vector Machine ✓ understood
A supervised learning algorithm that finds the optimal hyperplane to separate classes with maximum margin.
- Hinge Loss · you are here ✓ understood
Where it sits
Hinge Loss
Leads to
Nothing yet: a destination in its own right.
Explore nearby
Training Huber Loss A loss function that's quadratic for small errors and linear for large errors, robust to outliers. Training Cross-Entropy Loss A loss function for classification that measures the difference between predicted and true probability distributions. Foundations Classification A supervised learning task where the model assigns each input to one of a fixed set of categories, such as spam or not spam.