The idea is to take the template ( https://github.com/scikit-learn-contrib/project-template), build, test and document your estimator(s), and offer it to be housed within scikit-learn-contrib.
On 20 August 2017 at 08:36, Michael Capizzi <mcapi...@email.arizona.edu> wrote: > Thanks @joel - > > I wasn’t aware of scikit-learn-contrib. Is this what you’re referring to? > https://github.com/scikit-learn-contrib/scikit-learn-contrib > > If so, I don’t see any existing projects that this would fit into; could I > start a new one in a pull-request? > > -M > > > On Sat, Aug 19, 2017 at 2:47 AM, Joel Nothman <joel.noth...@gmail.com> > wrote: > >> this is the right place to ask, but I'd be more interested to see a >> scikit-learn-compatible implementation available, perhaps in >> scikit-learn-contrib more than to see it part of the main package... >> >> On 19 Aug 2017 2:13 am, "Michael Capizzi" <mcapi...@email.arizona.edu> >> wrote: >> >>> Hi all - >>> >>> Forgive me if this is the wrong place for posting this question, but I'd >>> like to inquire about the community's interest in incorporating a new >>> Transformer into the code base. >>> >>> This paper ( https://nlp.stanford.edu/pubs/sidaw12_simple_sentiment.pdf ) >>> is a "classic" in Natural Language Processing and is often times used as a >>> very competitive baseline. TL;DR it transforms a traditional count-based >>> feature space into the conditional probabilities of a `Naive Bayes` >>> classifier. These transformed features can then be used to train any >>> linear classifier. The paper focuses on `SVM`. >>> >>> The attached notebook has an example of the custom `Transformer` I built >>> along with a custom `Classifier` to utilize this `Transformer` in a >>> `multiclass` case (as the feature space transformation differs depending on >>> the label). >>> >>> If there is interest in the community for the inclusion of this >>> `Transformer` and `Classifier`, I'd happily go through the official process >>> of a `pull-request`, etc. >>> >>> -Michael >>> >>> _______________________________________________ >>> 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 >> >> > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > >
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