Exploration of Kernel Methods
Completion requirements
We will observe how the choice of a kernel affects the capabilities of SVM.
- We will use Paint Data to paint some data.
- Connect its output to SVM
- Add the Predictions widget; its data should come from Paint Data, its model from SVM.
- Connect a Scatter Plot to Predictions; color by classes, shapes by SVM's predictions.
Set the SVM to use a polynomial kernel. We will change the degree of the polynomial (setting d
).
- Paint some data that cannot be separated with linear kernel (d=1), but is separable using the quadratic kernel (d=2)
- Paint some data the requires d=3.
- What about this? Also, try it with RBF kernel.
Last modified: Friday, 11 March 2022, 1:53 PM