scale or not

[rohanag]

I'm getting different results for scaled and non scaled data. So can I get clarification, whether the data has to be scaled or not? Thank you.

[kkkkk]

For libsvm, the user guide and README recommend scaling and using the same scaling factors for training and testing sets.

Libsvm can save the scaling factors from the training data and apply it to the test data:

svm-scale -s scaling_parameters train_data > scaled_train_data
svm-scale -r scaling_parameters test_data > scaled_test_data

For the 1 vs 5 classifier, it is still necessary to filter out the unused digits first.

[mic00]

I believe (though please correct me if I'm wrong) that scaling will change the effective meaning of C, so I'm hesitant to use it here. My impression is that scaling is generally recommended in the real world, but in terms of the answers for this homework, I'm not sure.

[yaser]

Originally Posted by rohanag

I'm getting different results for scaled and non scaled data. So can I get clarification, whether the data has to be scaled or not? Thank you.

One of the side effects of scaling is that the resulting kernel will not be the same as without scaling . The homework problems are stated in terms of a given kernel.

[rohanag]

Thank you professor.

[kurts]

Originally Posted by yaser

One of the side effects of scaling is that the resulting kernel will not be the same as without scaling . The homework problems are stated in terms of a given kernel.

Apologies Professor, but I don't fully understand what you meant.

I understand that you are saying that the kernels are different whether you scale or not, but when you say the homework is stated in terms of a "given kernel", which kernel is that, scaled or not?

Everyone seems to take your statement as we should not scale, but it is not clear to me that is what you meant.

Again, sorry if I am being obtuse. Going on very little sleep, here

[yaser]

Originally Posted by kurts

Apologies Professor, but I don't fully understand what you meant.

I understand that you are saying that the kernels are different whether you scale or not, but when you say the homework is stated in terms of a "given kernel", which kernel is that, scaled or not?

Everyone seems to take your statement as we should not scale, but it is not clear to me that is what you meant.

Again, sorry if I am being obtuse. Going on very little sleep, here

Let's say that the input is scalar and the kernel given in the problem is . If you take the inputs in the training data set and normalize them (say compute their sample mean and standard deviation, , then define your new input variables to be ), then if you plug in these new variables in the kernel and use as your kernel (with ramifications on distances, margins, slack variables, etc.), you may be solving a different problem from the one without scaling.

Nothing wrong with scaling the training inputs in general, but the multiple-choice answers are based on the specific kernel and the given in the problem statements. If you change things, make sure that you are still solving the same problem that was stated.