Support Vector Machine, or SVM, is a supervised machine learning algorithm mainly used for classification tasks but can also handle regression.
SVM works by finding the best boundary (hyperplane) that separates data points of different classes. The goal is to maximize the margin between classes for better predictions.
How SVM Works
- Map data points in an N-dimensional space based on features.
- Find the hyperplane that best separates classes.
- Maximize the margin between the closest points of each class (support vectors).
- Predict new data based on which side of the hyperplane they fall.
SVM can also use kernel functions to handle non-linear data by transforming it into a higher-dimensional space.
Advantages of SVM
- Works well with high-dimensional data
- Effective for both linear and non-linear classification
- Memory efficient since it uses support vectors only
- Good generalization and less prone to overfitting
Disadvantages
- Can be slow for very large datasets
- Requires careful selection of kernel and parameters
- Hard to interpret for non-experts
Real-World Examples
- Email spam detection
- Image classification
- Face recognition systems
- Medical diagnosis (disease classification)
- Customer churn prediction
Conclusion
SVM is a powerful, flexible classification algorithm. By maximizing the margin between classes, it provides high accuracy for many real-world applications.
Citations
https://savanka.com/category/learn/ai-and-ml/
https://www.w3schools.com/ai/