from sklearn.multiclass import OneVsRestClassifier
clf = OneVsRestClassifier(ElasticNet())
should work.
This is tested here:
https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/tests/test_multiclass.py#L168
For setting the parameters by grid-search, you need to use the
"estimator__" prefix in your parameter grid.
parameter_grid = {"estimator__alpha": [1, 0.1, 0.01]}
On the implementation side, this relies on the fact that linear models
(ElasticNet included) implement "decision_function", which is in this case
just an alias for "predict".
Some people have opposed regressors implementing "decision_function". I
would be OK with removing it but we need a reliable way to tell whether an
estimator is a regressor or not so that the multiclass module can decide
whether to call predict (for regressors) or decision_function (for
classifiers)
(we have an is_classifier function in base.py but not an is_regressor one).
# Andreas used to oppose but changed his mind IIRC :-)
Mathieu
On Wed, Jul 23, 2014 at 12:02 AM, Michael Eickenberg <
michael.eickenb...@gmail.com> wrote:
> Conflicting messages, no, there is no explicit ElasticNetClassifier, but
> Manoj's proposition creates one:
>
> Concerning Manoj's point 2), you may also want to trying weighting in a
> different way, by centering the target variable y, i.e. if y is in {-1, 1},
> then do y <- y - y.mean(). This can help with the inevitable class
> imbalance in the OvR setting.
>
> Michael
>
>
> On Tue, Jul 22, 2014 at 4:56 PM, Vlad Niculae <zephy...@gmail.com> wrote:
>
>> Hi,
>>
>> The SGDClassifier supports elastic net regularization. You can make it
>> solve the SVM loss function or the logistic loss function by changing
>> the `loss=` parameter.
>>
>> Hope this helps,
>> Vlad
>>
>> On Tue, Jul 22, 2014 at 4:17 PM, Sheila the angel
>> <from.d.pu...@gmail.com> wrote:
>> > Hello All,
>> >
>> > Is it possible to perform classification using linear models such as
>> > ElasticNet?
>> >
>> > I tried the following -
>> >
>> >
>> >
>> > from sklearn.linear_model import ElasticNet
>> >
>> > iris = datasets.load_iris()
>> >
>> > X= iris.data
>> >
>> > y= iris.target
>> >
>> >
>> > clf= ElasticNet()
>> >
>> > clf.fit(X,y).predict(X[0])
>> >
>> >
>> > Which gives output value in decimal points.
>> >
>> > Any suggestion or link to an example will be very helpful.
>> >
>> >
>> >
>> > Thanks
>> >
>> > --
>> >
>> > Sheila
>> >
>> >
>> >
>> >
>> >
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>
>
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