Tuning SVM

Before we start working on the hyperparameters (which are typically a different set of parameters depending on the implementation), there are two aspects that are left to be clarified when working with an SVM algorithm.

The first is about the sensitivity of the SVM to variables of different scale and large numbers. Similar to other learning algorithms based on linear combinations, having variables at different scales leads the algorithm to be dominated by features with the larger range or variance. Moreover, extremely high or low numbers may cause problems in the optimization process of the learning algorithms. It is advisable to scale all the data at limited intervals, such as [0,+1], which is a necessary choice if you are working ...

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