Support Vector Machines (SVM) are a powerful set of supervised learning methods used primarily for classification and regression. SVMs have been around for a while and are particularly well suited for complex but small to medium-sized datasets.
Key Concepts
- Hyperplane: In SVM, the main goal is to find the best hyperplane that separates the data into classes.
- Support Vectors: These are the data points that lie closest to the decision boundary.
- Kernel Functions: They are used to map input vectors into higher-dimensional space to make the separation easier.
How SVM Works
- Select a Kernel Function: Choose the type of kernel function that works best for your data.
- Training the Model: Find the hyperplane that maximizes the margin between the classes.
- Making Predictions: Use the model to classify new data points based on the hyperplane.
Types of SVM
- Linear SVM: Suitable for linearly separable data.
- Non-linear SVM: Uses kernel functions to map data to higher-dimensional space.
- One-Class SVM: Useful for anomaly detection.
Example
Suppose you want to classify emails into spam and not spam. You would use a linear SVM with a suitable kernel function to separate the emails into these two classes.
For more information on how to implement SVM in Python, check out our SVM tutorial.
Visualizing SVM
Here's a simple visualization of a linear SVM:
As you can see, the hyperplane separates the two classes with the largest margin.
Resources
If you have any questions or need further clarification, please leave a comment below.