On Fri, Nov 04, 2011 at 07:22:15PM +0100, Andreas Müller wrote: > One of the reasons I want an MLP in sklearn is so it is easier > to compare with other learning algorithms on a wide range of > tasks.
I guess that this is one of the most compeling reasons to have them in. I tend to believe the MLPs are not 'machine learning without learning the machinery': they require a lot of domain knowledge and tweaking. It seems to me that this is not the kind of method that we want to advertise in the scikit: non experts might loose a lot of time on them. However, as they are definitely part of the state of the art, if we can get an implementation that is readable, debuggeable, and that performs reasonnably in terms of computational efficiency and prediction power, I think that integrating them is an option _as a reference for comparison_. We will need to point out clearly in the documentation the better implementations that we will not attempt to beat because they are too technical, either on the computation side (GPUs) or on the ML side (heaps of domain knowledge embedded). My 2 cents, Gaël ------------------------------------------------------------------------------ RSA(R) Conference 2012 Save $700 by Nov 18 Register now http://p.sf.net/sfu/rsa-sfdev2dev1 _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
