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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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