I would advise you to first implement those 2 new estimators outside of the scikit-learn code-base to not suffer from delays imposed by the scikit-learn review process (that lacks man-power). But if you follow strictly the scikit-learn code conventions and in particular the convention for making estimator class are scikit-learn compatible.
http://scikit-learn.org/dev/developers/contributing.html#rolling-your-own-estimator You might find this template project handy to automatically test that your estimators are scikit-learn compatible: https://github.com/scikit-learn-contrib/project-template Once your new estimators pass the test_common compliance suite, we can re-open a discussion for inclusion in the scikit-learn proper, based on the criteria in: http://scikit-learn.org/dev/faq.html#what-are-the-inclusion-criteria-for-new-algorithms If the scikit-learn developers decide that those estimators do not match those criteria you would still be welcome to contribute the project under the http://contrib.scikit-learn.org/ umbrella. -- Olivier Grisel ------------------------------------------------------------------------------ Find and fix application performance issues faster with Applications Manager Applications Manager provides deep performance insights into multiple tiers of your business applications. It resolves application problems quickly and reduces your MTTR. Get your free trial! https://ad.doubleclick.net/ddm/clk/302982198;130105516;z _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
