Question

If I have 200 features, and if each feature can have a value ranging from 0 to infinity, should I scale the feature values to be in the range [0-1] before I go ahead and train a LibSVM on top of it?

Now, suppose I did scale the values, and after training the model if I get one vector with its values or the features as input, how do I scale these values of the input test vector before classifying it?

Thanks
Abhishek S

Answer 1

If you’ve got infinite feature values, you’re not going to be able to use LIBSVM anyway.

More practically, scaling is generally useful so the kernel doesn’t have to deal with large numbers, so I would say go for it and scale. It’s not a requirement, though.

And as Anony-Mousse implied in the comments, please try running experiments with and without scaling so you can see the difference.

Now, suppose I did scale the values, and after training the model if I get one vector with its values or the features as input, how do I scale these values of the input test vector before classifying it?

You don’t need to scale again. You already did that in the pre-training step (i.e. data processing).