Hi,
I was asking if having lot of features might be a problem, but it shouldn’t
because with quantization it works just fine (default settings).
I will try tuning gamma but, again, with quantization it seems to be working
just fine.
Thanks,
From: Michael Eickenberg [mailto:[email protected]]
Sent: Tuesday, October 21, 2014 9:32 AM
To: [email protected]
Subject: Re: [Scikit-learn-general] SVM with rbf kernel
Dear Roberto,
On Tue, Oct 21, 2014 at 2:58 PM, Pagliari, Roberto
<[email protected]<mailto:[email protected]>> wrote:
I sometimes get weird results with SVM and rbf kernel in terms of false
positive/negative rates.
I suspect there may be numerical issues going on, because I’m not seeing the
same issues with linearSVC.
The rbf kernel might simply be overfitting. What happens if you make gamma
really large?
Does anyone know if rbf is constrained in terms of number of dimensions?
I am not sure I understand this question.
Unfortunately I cannot share the data I am using.
Thank you,
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