Dear All,
I'm using the Scipy SVM tool (the one derived from LibSVM I think). I have
training set and dataset. The training set is took within the dataset. The
training set is around the 10% of the dataset.
Before train my SVM is suggested to scale the data in order to get zero mean
and unit variance.
There are two options: - scale the training set, train the SVM, scale the whole
dataset, classify the dataset; - scale the whole dataset, take from it the
training set, train the SVM, classify the dataset.
The second seems to me more logic than the first but happens that I get
extremely better result using the first option than the second one!!!
Is it this normal ?? Probably is a dummy question but I have not too much
experience with that!
To scale the data I use sklearn.preprocessing.scale(MyData).
Any suggestions, test that I could do, is really really welcome!
Thanks,Solimyr
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