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Old 09-03-2012, 04:53 AM
Andrs Andrs is offline
Join Date: Jul 2012
Posts: 47
Default Re: Should SVMs ALWAYS converge to the same solution given the same data?


I also found the same problem when I used 10-fold with shuffle. See the following:

My guess is that if you have data with reasonably well separated features ("good data" in relation to our model), we should get the same result (or more or less the same results) if we have the same data that is processed in different sequences. But if we have a "difficult data", the classification margins may be very narrow and if we handle the data in different sequences we may get different results. I think that is the case with "one against all" in q5 where I do not get "repeatable results". I am skipping shuffle.
What is the purpose for randomizing the data in this case?
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