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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