How do you implement L2 regularization to improve SVM classification performance in Scikit-learn

Question

Can i know How do you implement L2 regularization to improve SVM classification performance in Scikit-learn?

Answer 1

L2 regularization in SVM is implemented via the "C" parameter in Scikit-learn’s SVC, controlling the trade-off between achieving a low-error margin and model complexity.

Here is the code snippet you can refer to:

In the above code we are using the following key points:

• Uses make_classification to generate a linearly separable dataset.
• Applies L2 regularization through the C parameter in SVC.
• Visualizes the decision boundary and evaluates classification accuracy.

Hence, L2 regularization in SVM via the C parameter balances model complexity and generalization, improving classification performance on unseen data.