polynomial svms and in-sample error
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 wrong with the asumption, that if i get those probabilities, that 0.5 is the threshold to get "the default" result?
On 16.11.2012, at 16:32, Jessica Streicher wrote:
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 should get an in-sample error lower or at least the same as the linear case. i'm worried i did something wrong there.
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