thanks Alexandre and Olivier. Indeed I don't expect to get better
performance in comparison to logistic regression with L1 regularization.
I guess my question was more on how to force the fit method to learn a
binary output. Using my code below, it assumes a regression problem. How
do I use Elastic Net for classification in practice?
Thanks,
Matthias
from sklearn.linear_model import ElasticNet
from sklearn import datasets
iris = datasets.load_iris()
X = iris.data
y = iris.target
idx=np.where(y!=2)[0] # select only labels 0 & 1
X=X[idx,:]
y=y[idx,:]
X /= X.std(0)
clf = ElasticNet(alpha=0.1, rho=0.7)
clf.fit(X,y)
In [53]: clf.predict(X)
Out[53]:
array([ 0.06641272, 0.11714499, 0.08138986, 0.12246076, 0.05626627,
0.12908023, 0.1049925 , 0.0920214 , 0.12729145, 0.09402743,
0.06158204, 0.10748362, 0.08871166, 0.04232499, -0.01524399,
0.04742352, 0.06723134, 0.09484605, 0.11079336, 0.07986891,
0.12294584, 0.1184487 , -0.00558262, 0.21839229, 0.15387029,
0.14806944, 0.16435028, 0.08187494, 0.07655918, 0.12777653,
0.13792298, 0.14888806, -0.00743711, -0.00461246, 0.09402743,
0.06592764, 0.0509505 , 0.09402743, 0.10168277, 0.0920214 ,
0.07938383, 0.20114128, 0.08138986, 0.21107048, 0.17015113,
0.14557832, 0.06689781, 0.09685208, 0.06158204, 0.08670563,
0.94830539, 0.94581427, 1.01780962, 0.90295459, 1.00186231,
0.92953343, 0.9950256 , 0.69927258, 0.9348492 , 0.87533988,
0.77078285, 0.91972051, 0.82780105, 0.97874475, 0.78022697,
0.91206518, 0.96610718, 0.79253101, 1.04727881, 0.8103328 ,
1.07750093, 0.85222232, 1.07868834, 0.93202455, 0.88846253,
0.92221163, 1.00435343, 1.10028496, 0.97625364, 0.70990412,
0.80501703, 0.76112148, 0.81847322, 1.11775321, 0.96610718,
0.9539547 , 0.98688517, 0.96480348, 0.84739163, 0.88266168,
0.90593079, 0.95313608, 0.8440819 , 0.70941904, 0.89329322,
0.83442053, 0.87300031, 0.88846253, 0.67117279, 0.86768454])
On 2/26/12 5:27 PM, [email protected]
wrote:
> ------------------------------
>
> Message: 5
> Date: Fri, 24 Feb 2012 18:18:15 +0100
> From: Alexandre Gramfort<[email protected]>
> Subject: Re: [Scikit-learn-general] ElasticNet for classification?
> To: [email protected]
> Message-ID:
> <cadeotzqprrh0qwfomr7yascp99zmthjxl54no9swo391ku4...@mail.gmail.com>
> Content-Type: text/plain; charset=ISO-8859-1
>
> hi,
>
> you could even if the squared loss is not really natural for
> classification settings.
>
> I'd be surprised if it gives a better result that a sparse logistic
> regression for example.
>
> Alex
>
> On Fri, Feb 24, 2012 at 6:13 PM, Matthias Ekman
> <[email protected]> wrote:
>> Hi,
>>
>> I was wondering is it possible to use the current implementation of
>> ElasticNet or LARS also for classification instead of regression?
>>
>> Thanks,
>> ?Matthias
>>
>> ------------------------------------------------------------------------------
>> Virtualization& Cloud Management Using Capacity Planning
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>> also focuses on allowing computing to be delivered as a service.
>> http://www.accelacomm.com/jaw/sfnl/114/51521223/
>> _______________________________________________
>> Scikit-learn-general mailing list
>> [email protected]
>> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
>
>
> ------------------------------
>
> Message: 6
> Date: Fri, 24 Feb 2012 19:22:38 +0100
> From: Olivier Grisel<[email protected]>
> Subject: Re: [Scikit-learn-general] ElasticNet for classification?
> To: [email protected]
> Message-ID:
> <CAFvE7K6S=n_qp22mictvy4hoqo1hxoglmn+pznueuqbff3b...@mail.gmail.com>
> Content-Type: text/plain; charset=UTF-8
>
> Yes and if you want multi class support you can use the
> sklearn.multiclass wrappers on them too.
>
> I would be interested to learn about any feedback where those models
> perform better / faster than the other sklearn classfiers.
>
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