Implementing Different SVM Kernels

Support Vector Machine are a type of supervised learning algorithm that can be used for classification or regression tasks. In simple terms, an SVM constructs a hyperplane or set of hyperplanes in a high-dimensional space, which can be used to separate different classes or to predict continuous variables. SVM kernels map input data into higher-dimensional feature spaces, enabling the model to separate complex patterns with greater precision.

Stepwise implementation of different SVM Kernels:

Step 1: Import Modules

Importing required modules.

• SVC from sklearn.svm to build SVM models
• make_classification to generate sample data
• numpy for numerical processing
• matplotlib.pyplot for visualization

Step 2: Generate Synthetic Dataset

Creating a 2-feature dataset so decision boundaries can be visualized easily.

Step 3: Helper Function to Plot Decision Boundaries

This function creates a grid, predicts labels across the grid and draws classification regions.

Step 4: Train SVM with Linear Kernel

Linear kernel draws a straight line between classes.

Output:

Step 5: Train SVM with Polynomial Kernel

Polynomial kernel generates curved, non-linear separation.

Output:

Step 6: Train SVM with RBF (Gaussian) Kernel

RBF kernel maps data to higher dimensions and creates smooth, complex surfaces.

Output:

Step 7: Train SVM with Sigmoid Kernel

Sigmoid behaves similarly to neural network activation functions.

Output: