Andy,
Shouldn't we also pull a ticket because using a uniform weight distribution
does not give the same results as running the SVM without weights? (See my
original post on the problem.)
Thanks,
Anne
On Mon, Jul 22, 2013 at 4:50 AM, Andreas Mueller
<[email protected]>wrote:
> Hi Daniel.
> Could you please open an issue and maybe provide a sample script that
> demonstrates that
> changing the sample weights doesn't change the decision function?
>
> That sounds like a bug.
>
> Cheers,
> Andy
>
>
> On 07/21/2013 02:08 PM, Daniel Vainsencher wrote:
>
> I encountered similar problems.
> Weighting libsvm inputs under sklearn seems unsafe at any speed...
>
>
> Amusingly enough, I think the pretty demo at [1] demonstrates the problem.
> Notice how the white samples are really dominant... but the decision boundary
> is consistent with uniform weights.
>
>
> If you change the script slightly, its obvious changing the weight ratio does
> not change the displayed decision boundary, so it is not a problem of merely
> scaling or interaction with C.
>
> This may be related to the fact that sample weighting in libsvm itself does
> not appear to be a stable core feature; [2] implies that a special version of
> libsvm is needed. Or is that already taken into account?
>
>
> [1]
> http://scikit-learn.org/stable/auto_examples/svm/plot_weighted_samples.html
>
> [2] http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/#weights_for_data_instances
>
> --
>
> Daniel Vainsencher
>
>
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