Hi again!
This might be more of a statistical question, but anyway:
If i train several support vector machines with different degrees of
polynomials, and as result, get that higher degrees not only have a higher test
error, but also a higher in-sample error, why is that?
I would assume i
Actually i think i found the problem, its something about the probability model
again as it seems, if you just take the normal predictions everythings good.
Man does that probability stuff absolutely not work properly. Any suggestions
how to do ROC curves without it?
Or am i just generally
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