On 01/14/2015 02:06 AM, Joel Nothman wrote:
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...
Well, there is "stacking" but that is rarely used in practice, I think.
FeatureUnion is also more of an engineering tool than a theoretical one...
On 14 January 2015 at 13:21, Andy <[email protected]
<mailto:[email protected]>> 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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