A powerful machine learning algorithm used for classification and regression tasks.
🧠 What is SVM?
Support Vector Machine (SVM) is a supervised learning model that finds the optimal hyperplane to separate data points into distinct classes. It maximizes the margin between the closest points (support vectors) of different classes.
🔍 Key Concepts
- Hyperplane: A decision boundary in N-dimensional space.
- Margin: The distance between the hyperplane and the nearest data points from each class.
- Support Vectors: Data points lying closest to the hyperplane.
📈 How SVM Works
- Data Representation: Features are mapped into a high-dimensional space.
- Optimal Separation: Finds the hyperplane with the largest margin.
- Kernel Trick: Uses kernels (e.g., linear, RBF) to handle non-linear data.
📌 Example: Max-margin Classifier
✅ Advantages & Disadvantages
| Pros | Cons |
|---|---|
| Effective in high-dimensional spaces | Computationally intensive for large datasets |
| Uses kernel methods for non-linear problems | Sensitive to noisy data and outliers |
📌 Applications
- Text Classification: Spam detection, sentiment analysis.
- Bioinformatics: Protein classification, gene expression analysis.
- Image Recognition: Handwritten digit classification (e.g., MNIST dataset).
📘 Further Reading
For deeper insights, explore our tutorial on Machine Learning Basics to understand the broader context of supervised learning models.