Dear Olivier,
thanks for your reply.
On 25 September 2013 10:39, Olivier Grisel <olivier.gri...@ensta.org> wrote:
> LogisticRegression is a already multiclass classifier by default using
> the One vs Rest / All strategy by default (as implemented internally
> by liblinear which LogisticRegression is a wrapper of). So you don't
> need to use OneVsRest in this case.
>
> If you want more info on multiclass reductions here is the doc:
> http://scikit-learn.org/stable/modules/multiclass.html
>
>
This morning I checked the source for LogisticRegression in
sklearn/linear_model/logistic.py and realized that by default it performs
multiclass classification
(this is not explained in the user guide
http://scikit-learn.org/stable/modules/linear_model.html#logistic-regression,
though).
> Is there a way to check how the normalization of the data is performed?
>
> What normalization? There is no normalization unless you do it
> yourself with one of those tools and a pipeline:
>
> http://scikit-learn.org/stable/modules/preprocessing.html
You are right, I got confused with LinearRegression that display something
like *normalize=None* when performing the fit.
I scribbled about checking for it in the documentation on a piece of paper
and got confused when I was looking at the documentation for
LogisticRegression and wrote the email.
There are still a few things that are not clear to me from the
documentation. Can you customize the classifier to perform a different
decision function?
Or can I "hook" a preprocessing step to be applied to the data (I am
thinking for example for polynomial logistic regression, where from the
original dataset
I want to "build" all the features of order 2, for example. I am just
asking for educational purposes, I guess there are more appropriate
methods).
Other questions that I have:
1. can I use a norm different from l1 or l2?
2. similarly, can I define my own cost function?
3. can I try alternative optimization algorithms?
I am sure these answers are in the documentation, but I couldn't find them
in the TOC (in the user guide) and I have encountered them, yet.
Thanks again for the help!
Cheers,
Luca
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