Dear Roberto,
On Tue, Oct 21, 2014 at 4:27 PM, Pagliari, Roberto <rpagli...@appcomsci.com>
wrote:
> From the documentation:
>
>
>
> *“The implementations is a based on libsvm. The fit time complexity is
> more than quadratic with the number of samples which makes it hard to scale
> to dataset with more than a couple of 10000 samples.”*
>
>
>
> Does that mean that the results may also not be very accurate with that
> many samples (for example, numerical issues)?
>
>
>
It essentially means a blow-up in memory. SVM with a kernel that is not
linear is evaluated in dual (kernel-) space, which involves solving a
quadratic programming problem of complexity around n_samples x n_samples.
While one can try to use more or less effective tricks to keep the memory
usage down (e.g. using only in some way "relevant" samples), the main
tendency is still there.
A high number of samples is usually a trigger for people to use linear
models such as the SVM with a linear kernel or logistic regression, whos
complexity is then a function of the dimension of the feature space.
If you are concerned with numerical stability and results with LibLinear
(LinearSVM) are satisfactory, then you should try using
`sklearn.svm.SVC(kernel="linear")` and check if you obtain similar results.
HTH,
Michael
>
>
> Thank you,
>
>
>
> *From:* Pagliari, Roberto [mailto:rpagli...@appcomsci.com]
> *Sent:* Tuesday, October 21, 2014 9:39 AM
>
> *To:* scikit-learn-general@lists.sourceforge.net
> *Subject:* Re: [Scikit-learn-general] SVM with rbf kernel
>
>
>
> 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:michael.eickenb...@gmail.com
> <michael.eickenb...@gmail.com>]
> *Sent:* Tuesday, October 21, 2014 9:32 AM
> *To:* scikit-learn-general@lists.sourceforge.net
> *Subject:* Re: [Scikit-learn-general] SVM with rbf kernel
>
>
>
> Dear Roberto,
>
>
>
> On Tue, Oct 21, 2014 at 2:58 PM, Pagliari, Roberto <
> rpagli...@appcomsci.com> 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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