Main Concept
An SVM is a traditional machine learning algorithm used for classification and regression tasks. It finds the optimal boundary that separates data points of different classes with the maximum margin.
How It Works
SVMs plot data points in high-dimensional space and find a line (or hyperplane) that best separates classes. It maximizes the distance between the boundary and the nearest points from each class.
Characteristics
- Traditional ML — not deep learning
- Effective with smaller datasets — doesn’t require massive amounts of data like deep learning
- Good for binary classification — though can be extended to multi-class
- Computationally expensive — training time increases with dataset size
Use Cases
- Text classification
- Spam detection
- Image classification (on smaller datasets)
- Medical diagnosis
SVM vs. Deep Learning
| SVM | Deep Learning | |
|---|---|---|
| Data requirement | Smaller datasets OK | Needs massive datasets |
| Training time | Faster on small data | Slower but scales to big data |
| Interpretability | More interpretable | Black box |
| Performance | Good for small data | Better for large data |
AIF-C01 Context
SVMs are “traditional ML” — the exam wants you to know when to use traditional ML vs. deep learning. If you have a small, well-defined dataset, SVM might be better than a complex neural network.