>
> > (this is not explained in the user guide
> >
> http://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
> ,
> > though).
>
> All our classifiers support multiclass classification and this is
> documented in various places.
>
I am sorry, but I went into the user documentation for logistic regression
and multiclass classification and didn't find any information about it
>
> > 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?
>
> You can subclass it and override the decision_function method.
>
> > 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
>
> You can implement a polynomial expansion as a transformer object, then
> tie it to logistic regression using a sklearn.pipeline.Pipeline. See
> the developer's docs, esp. the "Rolling your own estimator" guide [1],
> or our recent paper [2] for the conventions.
>
Thanks, I'll look into it
>
> > 1. can I use a norm different from l1 or l2?
>
> For what?
>
for the penalty in LogisticRegression, but looking at the code it seems it
is not possible.
>
> > 2. similarly, can I define my own cost function?
>
> No, unless you hack the source code.
> > 3. can I try alternative optimization algorithms?
>
> You can try SGDClassifier(loss="log") which also implements
> one-vs.-all logistic regression, but trained with stochastic gradient
> descent.
>
Isn't there an interface to implement my own optimizer and see the
performances?
>
>
> [1]
> http://scikit-learn.org/stable/developers/index.html#rolling-your-own-estimator
> [2] http://staff.science.uva.nl/~buitinck/papers/scikit-learn-api.pdf
>
>
Thanks for the links, I'll go through them!
Cheers,
Luca
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