@Alexandre, @Mathieu: Thanks for these hints. I'll give it a try.

So setting epsilon=0 and C to a large value should result in a
regression in the L1 norm with almost no regularization of w, right?.
One thing that just crossed my mind. Would it be possible in a linear
SVR setting to let the norm(w) term [in the primal objective funtion]
be in the L1 norm in order to get some sparsity like in Lasso?

Florian

On 13 January 2014 08:15, Mathieu Blondel <math...@mblondel.org> wrote:
> Here's an example that illustrates the use of LinearSVR for doing robust
> regression with lightning:
> https://github.com/mblondel/lightning/blob/master/examples/plot_robust_regression.py
>
> Regarding epsilon=0, it is a good choice for LinearSVR but less so for
> (kernel) SVR. epsilon=0 leads to completely dense solutions in the dual and
> so the kernel expansion (used in the predict method) might be slow to
> evaluate for large datasets.
>
> Mathieu
>
>
>
> On Sun, Jan 12, 2014 at 3:33 AM, Alexandre Gramfort
> <alexandre.gramf...@telecom-paristech.fr> wrote:
>>
>> hi,
>>
>> did you try SVR ? eventually setting epsilon to 0.?
>>
>> if it's too slow have a look at lightning new LinearSVR estimator.
>>
>> Alex
>>
>>
>>
>>
>> On Sat, Jan 11, 2014 at 7:28 PM, florian.wilh...@gmail.com
>> <florian.wilh...@gmail.com> wrote:
>>>
>>> Hi,
>>>
>>> at Blue Yonder we often use Scikit-Learn but are sometimes missing
>>> more robust regression methods that are not based on the L2 norm.
>>> So far I only knew Theil-Sen as a linear regression method with only a
>>> single explanatory variable. The work of Xin Dang, Hanxiang Peng,
>>> Xueqin Wang and Heping Zhang extend the method to n explanatory
>>> variables. So it should perfectly fit into the sklearn.linear_model
>>> subpackage I think. Where is the line drawn between functionality that
>>> should go into StatsModels and into Scikit-Learn with respect to
>>> regression methods?
>>>
>>> Florian
>>>
>>> On 10 January 2014 19:18, Skipper Seabold <jsseab...@gmail.com> wrote:
>>> > Hi,
>>> >
>>> > There have been some implementations of Theil-Sen floating around for
>>> > inclusion in statsmodels, but no PRs yet. IMO it might fit in a little
>>> > better in statsmodels.robust than sklearn unless their are some aspects of
>>> > Theil-Sen I'm not familiar with.
>>> >
>>> > Skipper
>>> >
>>> > Sent from my mobile
>>> >
>>> >> On Jan 10, 2014, at 12:16 PM, "florian.wilh...@gmail.com"
>>> >> <florian.wilh...@gmail.com> wrote:
>>> >>
>>> >> Hi,
>>> >>
>>> >> I'd like to add a Theil-Sen estimator for a multiple linear regression
>>> >> problem to Scikit-Learn as described in the paper:
>>> >> http://home.olemiss.edu/~xdang/papers/MTSE.pdf
>>> >> Is anyone already working on this or are there any objections
>>> >> regarding the inclusion of a Theil-Sen estimator into Scikit-Learn?
>>> >>
>>> >> Best regards,
>>> >>
>>> >> Florian Wilhelm
>>> >>
>>> >>
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