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Impact of choice of kernel on model complexity

Good afternoon,

I am currently revising all the material from this course and I am wondering how the choice of kernel affects the complexity of a SVM model? Do we simply treat the choice of kernel as a hyper parameter and try different ones while grid searching over the regularisation variable lambda?

Great question! I also have a related question, should the grid-search be done separately for each variable? Testing all the possible kernel-variable combinations.

Any advice on how to choose the best kernel?

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