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Can standardisation of predictors make SVM regression perform worse?
Standardisation = scaling so that mean of distribution is zero and variance equal to one.
Are there any circumstances when applying standardisation to predictors would result to considerably worse (signal to noise ratio) performance of SVM (Support Vector Machine) regression?