Hello, As mentioned by Roman, you can try the one-class scikit-learn algorithms such as OneClassSVM, IsolationForest, LocalOutlierFactor (with the private predict method) or EllipticEnvelope.
Hope this helps Nicolas On Fri, Jun 30, 2017 at 3:39 PM, Roman Yurchak <rth.yurc...@gmail.com> wrote: > Hello Ruchika, > > I don't think that scikit-learn currently has algorithms that can train > with positive and unlabeled class labels only. However, you could try one > of the following compatible wrappers, > - http://nktmemo.github.io/jekyll/update/2015/11/07/pu_classif > ication.html > - https://github.com/scikit-learn/scikit-learn/pull/371 > > (haven't tried them myself). > > Also, you could try one class SVM as suggested here > https://stackoverflow.com/questions/25700724/binary-semi- > supervised-classification-with-positive-only-and-unlabeled-data-set > > -- > Roman > > > > > On 30/06/17 16:06, Ruchika Nayyar wrote: > >> Hi All, >> >> I am a scikit-learn user and have a question for the community, if >> anyone has applied any available machine learning algorithms in the >> scikit-learn package for data with positive and unlabeled class only? If >> so would you share some insight with me. I understand this could be a >> broader topic but I am new to analyzing PU data and hence can use some >> help. >> >> Thanks, >> Ruchika >> >> >> >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn >> >> > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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