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

[Andreas Mueller]

Hi Jakob.
Sounds like you read up on all the right things.
Indeed sounds like a case for scikit-learn-contrib.
I think the most common pitfall is that it might take some time for 
someone to review the project to get merged into scikit-learn-contrib.
I'm not sure if there's a backlog right now.
Though Alex Gramfort might be interested in this, which might speed up 
the process ;)

Cheers,
Andy

On 11/6/18 9:07 AM, Jakob Zeitler 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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