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).

  1. Paint some data that cannot be separated with linear kernel (d=1), but is separable using the quadratic kernel (d=2)
  2. Paint some data the requires d=3.
  3. What about this? Also, try it with RBF kernel.

Zadnja sprememba: petek, 11. marec 2022, 13.53