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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