If you are going to make a scikit-learn-contrib project, we recently updated and simplified the project template:
On Tue, 6 Nov 2018 at 18:26, Jakob Zeitler <m...@jakob-zeitler.de> wrote: > Dear sklearners, > > I have been working on a rank-based outlier detection algorithm (RBDA) > developed here at Syracuse, of which the code I would like to contribute to > sklearn as it gives a viable alternative to established algorithms such as > LOF (https://www.tandfonline.com/doi/abs/10.1080/00949655.2011.621124) > > Should I be fine if I keep to the general contribution rules regarding > estimators? ( > http://scikit-learn.org/stable/developers/contributing.html#rolling-your-own-estimator) > Are they up to date? > > Because RBDA is <200 citations, I assume it will not pass the inclusion > criteria ( > http://scikit-learn.org/stable/faq.html#what-are-the-inclusion-criteria-for-new-algorithms) > therefore I assume I am dealing with a case of “scikit-learn-contrib” as > discussed here ( > https://github.com/scikit-learn-contrib/scikit-learn-contrib/blob/master/workflow.md > ) > > If anyone can share common pitfalls of that process, that would be great! > > Thanks a lot, > Jakob > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > -- Guillaume Lemaitre INRIA Saclay - Parietal team Center for Data Science Paris-Saclay https://glemaitre.github.io/
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