An SVM provides a variety of options to build a nonlinear classifier. We need to build a nonlinear classifier using various kernels. For the sake of simplicity, let's consider two cases here. When we want to represent a curvy boundary between two sets of points, we can either do this using a polynomial function or a radial basis function.
params = {'kernel': 'linear'}
Replace this line with the following:
params = {'kernel': 'poly', 'degree': 3}
This means that we use a polynomial function with degree 3. If you increase the degree, this means we allow the polynomial to be curvier. However, curviness comes at a cost in the sense that it will take more time to train because it's more computationally expensive.
params = {'kernel': 'poly', 'degree': 3}
Replace this line with the following one:
params = {'kernel': 'rbf'}
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