Yes, and I have no problem with that. My train and test attributes are exactly consistent with each other. My problem is that I get different results while run two above scenarios. And certainly this is due to the size of my test set examples differ in each scenario and so the tf/Idf exampleset differs too.
As you know, tf/idf weight for a specific attribute can be calculated like this:
Normalized value = TF value * weight
Weight = log ( NumberOfDocuments / NumberOfDocumentsWhichContainTheAttribute)
So the value of weight depends on the size of my test set. Now, I don't know how much data I should have in my test sets? 10? 20? just 1? Maybe I should not use tf/idf weighting for text stream classification at all. Please help me.
Thanks in advance.