The Support Vector Machine (SVM) is a powerful tool for classification tasks. It’s known for its versatility, especially when combined with a Radial Basis Function (RBF) kernel. Understanding how SVM with an RBF kernel works is crucial for making informed decisions about its hyperparameters, specifically the regularization parameter C and the kernel coefficient (gamma). Visualization is a fantastic way to grasp the inner workings of this model and the effect of these hyperparameters.
Before diving into visualization, let’s quickly recap what SVM is. It’s a supervised learning algorithm used for classification and regression tasks. SVM identifies the best hyperplane that separates different classes in the input data. In cases where the data isn’t linearly separable, the RBF kernel comes to the rescue.
The RBF Kernel: A Non-linear Transformer
Before delving into the captivating world of SVM decision functions, let’s recap the RBF kernel’s essence. The RBF kernel, also known as the Gaussian kernel, is one of the most widely used kernels in SVM classification. It leverages the concept of similarity to transform data into a higher-dimensional space, making it more amenable to linear separation.
The beauty of the RBF kernel lies in its ability to capture non-linear decision boundaries with finesse. It achieves this by assigning higher similarity scores to data points that are closer to each other in the transformed space. The kernel function elegantly reflects the intuitive notion that points that are close to each other should have higher influence on the decision boundary.
Streamlit is an excellent tool for creating interactive data visualizations and exploring SVM with RBF kernels. With Streamlit, you can build interactive apps to observe how changes in C and gamma affect the decision boundary and the overall performance of the SVM model.
By changing these hyperparameters interactively, you can immediately see how the decision boundary adapts, providing valuable insights into the model’s behavior.