here is the PR

https://github.com/scikit-learn/scikit-learn/pull/438

Alex

On Fri, Nov 11, 2011 at 3:53 AM, Gael Varoquaux
<[email protected]> wrote:
> On Thu, Nov 10, 2011 at 09:58:16PM -0500, Alexandre Gramfort wrote:
>> To me this is wrong to not apply such a scaling by n_samples. To
>> motivate this, just look as the gist and you will see that if you don't
>> do it then C / alpha needs to be changed if you duplicate every sample.
>> This is particularly problematic with the cross-validation as you end
>> up finding a C/alpha adapted to the size of the training folds rather
>> than the full data. Think about the refit in GridSearchCV.
>
>> Let me know what you think but I feel we should fix this.
>
> Yes, we should fix this, but we are breaking backward compatibility by
> doing this. Thus people will scratch there head and look at their results
> wondering why hell broke loose when they updated their version of the
> scikit.
>
> Thus we need to plan a way forward for these changes:
>
>  1. I think that we should add a parameter to the different objects that
>    we are changing, that sets the scaling behavior. For one release or
>    two, the parameter is set by default in such a way that the behavior
>    is not changed. After this, we change the default, and yet-again
>    after a couple release, we remove it.
>
>  2. We need a warning that things will change in a later release and the
>    the right way to follow these changes is to switch the value of the
>    parameter.
>
>  3. We need a clear statement in whats_new.rst
>
>  4. Maybe we need to have a file for internal use where we document the
>    future changes, so that we don't forget this (and other things).
>
> My 2 cents,
>
> Gaël
>
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