Thank you all.
I tried the OneVsRestClassifier as
iris = datasets.load_iris()
X = iris.data
y = iris.target
X /= X.std(0)
clf = OneVsRestClassifier(ElasticNet(alpha=0.25, l1_ratio=0.5)).fit(X,y)
y_pred = clf.predict(X)
This works however
clf.predict_proba(X)
gives error
AttributeError: 'ElasticNet' object has no attribute 'predict_proba'
So how do I obtain the class probability along with classification?
On 23 July 2014 03:21, Mathieu Blondel <math...@mblondel.org> wrote:
> 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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