How do you use SVC with RBF kernel for classification on a non-linear dataset in Scikit-learn

Question

Can you tell me How do you use SVC with RBF kernel for classification on a non-linear dataset in Scikit-learn?

Answer 1

SVC with an RBF kernel in Scikit-learn efficiently handles non-linear classification by mapping input features into a higher-dimensional space where classes become linearly separable.

Here is the code snippet you can refer to:

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

• Uses make_moons to generate a non-linear dataset.
• Applies the RBF kernel in SVC to capture complex decision boundaries.
• Evaluates model accuracy and visualizes decision boundaries.

Hence, SVC with an RBF kernel in Scikit-learn effectively classifies non-linear data by transforming it into a higher-dimensional space where it becomes linearly separable.