Overview
SVMs are powerful classifiers that work by finding the 'maximum margin hyperplane'—the boundary that provides the largest possible distance between the nearest points of different classes (called support vectors).
Key Concepts
- Hyperplane: The decision boundary.
- Margin: The distance between the hyperplane and the nearest data points.
- Kernel Trick: A technique that allows SVMs to handle non-linearly separable data by mapping it into a higher-dimensional space where a linear separation is possible.
Strengths
- Effective in high-dimensional spaces.
- Robust against overfitting, especially in high-dimensional space.
- Memory efficient.