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 > > > > > ------------------------------------------------------------------------------ > Want fast and easy access to all the code in your enterprise? Index and > search up to 200,000 lines of code with a free copy of Black Duck > Code Sight - the same software that powers the world's largest code > search on Ohloh, the Black Duck Open Hub! Try it now. > http://p.sf.net/sfu/bds > _______________________________________________ > Scikit-learn-general mailing list > Scikit-learn-general@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > ------------------------------------------------------------------------------ Want fast and easy access to all the code in your enterprise? Index and search up to 200,000 lines of code with a free copy of Black Duck Code Sight - the same software that powers the world's largest code search on Ohloh, the Black Duck Open Hub! Try it now. http://p.sf.net/sfu/bds _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
