2013/10/19 Andreas Mueller :
> The multi-class documentation says
> "You don’t need to use these estimators unless you want to experiment
> with different multiclass strategies:
> all classifiers in scikit-learn support multiclass classification
> out-of-the-box. Below is a summary of the classifie
On 09/25/2013 05:31 AM, Lars Buitinck wrote:
> 2013/9/25 Luca Cerone :
>> I am sorry, but I went into the user documentation for logistic regression
>> and multiclass classification and didn't find any information about it
> Hm, maybe we should put this in a more prominent place like the
> tutorial
On 25 September 2013 13:55, Olivier Grisel wrote:
> 2013/9/25 Luca Cerone :
> >> > (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 classificat
2013/9/25 Luca Cerone :
>> > (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
>> 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.
While true, this can be misleading. You're just changing the final
st
2013/9/25 Luca Cerone :
> I am sorry, but I went into the user documentation for logistic regression
> and multiclass classification and didn't find any information about it
Hm, maybe we should put this in a more prominent place like the
tutorial. I'll check the docs if I have time.
> for the pen
>
> > (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 documentat
2013/9/25 Luca Cerone :
> 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#logisti
Dear Olivier,
thanks for your reply.
On 25 September 2013 10:39, Olivier Grisel 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
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 i
Ok training a OneVsAll classifier it was actually easy.
To inspect the individual classifier I can use the .estimators_ attribute?
Do the estimators in it correspond to the .classes_ that is the
estimators_[0] is trained to recognize .classes_[0] vs the other and so on?
Is there a way to check how
Dear all,
I am practising with scikit-learn to solve multiclass classification
problems.
As an exercise I am trying to build a model to predict the digits dataset
available with scikit-learn.
Ideally I would like to solve this using logistic regression, building a
predictor for each digit (one v
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