juniorSVM
What is kernel in SVM?
Updated May 17, 2026
Short answer
A kernel transforms data into higher dimensions to make it separable.
Deep explanation
Kernel functions allow SVM to handle nonlinear data by implicitly mapping input features into higher-dimensional space.
Real-world example
Used in image classification where data is not linearly separable.
Common mistakes
- Thinking kernel explicitly transforms data.
Follow-up questions
- What is kernel trick?
- Which kernel is most common?