[scikit-learn] Outlier Detection: Contributing a new Estimator: Rank-Based Outlier Detection

[Guillaume Lemaître]

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 <mail at 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
>
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-- 
Guillaume Lemaitre
INRIA Saclay - Parietal team
Center for Data Science Paris-Saclay
https://glemaitre.github.io/
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