I wonder if these ensembles, while common, are too non-standard. Are there
well-analysed variants of these models in the literature, or standard ways
to configure them? If not, perhaps this is best presented as an example
rather than avaialable in the library...
On 14 January 2015 at 13:21, Andy <t3k...@gmail.com> wrote:
> Hi Sebastian.
> I think this might be useful as these times of algorithms are often used
> in competitions.
> It would also be nice to provide a transform method, so that one could
> also learn another model on top
> (like here
>
> http://zacstewart.com/2014/08/05/pipelines-of-featureunions-of-pipelines.html
> ).
>
> Cheers,
> Andy
>
>
> On 01/10/2015 07:13 PM, Sebastian Raschka wrote:
> > Hi,
> >
> > I wrote a short blog post about implementing a conservative majority
> rule ensemble classifier in scikit-learn someone asked me whether this
> would be interesting for the scikit-learn library.
> >
> > The idea behind it is quite simple: Using the weighted or unweighted
> majority rule from different classification models (naive Bayes, Logistic
> Regression, Random Forests etc.) to predict the class label.
> >
> > clf1 = LogisticRegression()
> > clf2 = RandomForestClassifier()
> > clf3 = GaussianNB()
> >
> > eclf = EnsembleClassifier(clfs=[clf1, clf2, clf3], weights=[1,1,1])
> >
> > for clf, label in zip([clf1, clf2, clf3, eclf], ['Logistic Regression',
> 'Random Forest', 'naive Bayes', 'Ensemble']):
> > scores = cross_validation.cross_val_score(clf, X, y, cv=5,
> scoring='accuracy')
> > print("Accuracy: %0.2f (+/- %0.2f) [%s]" % (scores.mean(),
> scores.std(), label))
> >
> > (more details in the blog post:
> http://sebastianraschka.com/Articles/2014_ensemble_classifier.html)
> >
> > If you would consider this as useful, let me know, and I would be happy
> to contribute it to the scikit-learn library.
> >
> > Best,
> > Sebastian
> >
> >
> >
> >
> >
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