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I am using the SVM classifier from Scikit Learn. I was wondering is there is a know-best-practice when it comes to normalization. I'm using different normalization tecniques, but all my normalized data are now between 0 and 1.

Using GridSearchCV, I always end up picking the linear kernel for the SVC. Would it be better if I were to normalize having my values between -1 and 1?

I feel like the answer is yes, thinking about situations like this one: enter image description here

But I wanted to know if there is an official answer. Thanks a lot

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Nope. There no definite answer. You cannot arrive at a conclusive decision point unless you experiment and analyze for yourself on the data with various kernel tricks. So feel Free to experiment as much as you can to learn to your best abilities

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