> Don't you think that I could also benchmark models that are not > implemented in sklearn? For instance, I could write a wrapper > DeepNet(...) with fit() and predict(), and which uses internally theano > to build a ANN? In this way, I could benchmark complex deep networks > beyond what will be possible with the new sklearn ANN module.
I am personally less interested in that. We have already a lot in scikit-learn and more than enough to test the model selection code. The focus should be on providing code that is readily-usable. I am worried that such task will be very time consuming and will not move us much closer to code that improves model selection in scikit-learn. Gaël ------------------------------------------------------------------------------ Dive into the World of Parallel Programming The Go Parallel Website, sponsored by Intel and developed in partnership with Slashdot Media, is your hub for all things parallel software development, from weekly thought leadership blogs to news, videos, case studies, tutorials and more. Take a look and join the conversation now. http://goparallel.sourceforge.net/ _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
