On 02/05/2013 07:09 PM, Lars Buitinck wrote: > 2013/2/5 Gael Varoquaux <gael.varoqu...@normalesup.org>: >> Do we need such genericity? Are there real practical gains to such >> modularity? My gut feeling would be to move forward with something >> simple, and make it performant statistical and computationaly for the >> common cases. > Perhaps not, but would it hurt to have both? > > 2013/2/5 Olivier Grisel <olivier.gri...@ensta.org>: >> Argl. This does not feel like linear model model at all. I would >> rather put the transformer in sklearn.kernel_approximation . > Indeed it doesn't :) +1 for kernel_approximations. > I feel it should be next to the kernel approximations but I don't like the module name :-/ I would call it feature extraction but that is already taken. And "feature learning" doesn't really fit if there is no learning...
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