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 <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 <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 <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 <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
