Hi everybody.
Long and general mail coming on.
TL;DR version: do we want to plan for the future?

Today I read this blog post on the scope of open source projects:
http://brianegranger.com/?p=249

It made me dig up an old mail draft I wrote after reading a post by Gael:
http://www.slideshare.net/eleddy/i-wish-i-knew-how-to-quit-you


I realized then that we don't really make any plans. Sometimes people 
(mostly me)
tag some issues to certain releases but that's it.
There is some vague idea, pushed mainly by Gaƫl that we want to do a
1.0 in the not-so-far future, but there is no list of features that we 
want (I created
a milestone and assigned random bits, not sure if any one noticed).

I wanted to ask: should we try to make plans? We get a lot of PRs and
have more and more contributors and I think it might be nice
if we had some form of road map to give everything a bit more direction.

I know that people mostly contribute algorithms they are using in research,
and that is great, because that makes for high-quality code.
I am not sure, though, for how long the "hey look, I coded this cool 
estimator
which I used in my latest paper" strategy is feasible.

There are also several classes of algorithms that we haven't really 
touched on that
might be in the scope and might "creep" into sklearn without really any 
discussion.

I'm thinking mainly about ranking, collaborative filtering, structured 
prediction (in particular sequences),
metric learning, graphical models (and some more).


Maybe the project is still small enough that our current approach might 
work,
but with more and more new contributors, I thought it might be good to 
think a little
bit about where we want to go.


Cheers,
Andy

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