> Also on the subject of @amueller's recent PR on hyperparameter > optimization, I was wondering if anyone is interested in packaging > optimization algorithms for tree-structured spaces. There are several > algorithms in the works by myself and others (which build on many of > the other algorithms already in sklearn) so I think it's a good time > for a discussion on this kind of interface. Do you think that > algorithms for optimizing cost functions over this sort of search > space should be in sklearn, or should they be in another package > (scikit-bayesian-optimization, aka skbo?), which would certainly be > designed to work well with sklearn?
I think that for now, they are too specific and corner-case to go in the scikit-learn. The scikit still has a lot of ground to cover in terms of quality implementation of more classic problems, and I would like to focus on these. In particular, I would really like to converge on the API and do a 1.0 release. My 2 cents, Gaël ------------------------------------------------------------------------------ Don't let slow site performance ruin your business. Deploy New Relic APM Deploy New Relic app performance management and know exactly what is happening inside your Ruby, Python, PHP, Java, and .NET app Try New Relic at no cost today and get our sweet Data Nerd shirt too! http://p.sf.net/sfu/newrelic-dev2dev _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
