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I am working on a problem to predict revenue generated by a film. I am using sklearn's support vector regression algorithm with polynomial kernel. I tried to find the degree which gives best accuracy using default value of regularization parameter. But, I got error percentage in the 7 digit range. So, I decided to increase variance, by tuning the regularization parameter.

So should I first assume a degree and find the regularization parameter which gives best result or vice versa?
Or is there something else that I should consider?

V K
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1 Answers1

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Generally, doing a grid search with degree and regularization parameter is a common practice. There is some information about this is sklearn here:

https://scikit-learn.org/stable/modules/grid_search.html

This will allow you to make a dictionary of the kernels that you can try (rbf, poly, etc) and their respective hyperparameters and the regularization parameter and attempt to find the best one.

scottbaker
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