+1 on those suggestions
My impression is the core value of sklearn is being readily useful to
practitioners of machine learning algorithms. We shouldn't be afraid to
deprecate modules that aren't being used. I think much of the core effort
will be in making the library more internally consistent, and defining how
we want the library to interact with other libraries such as scikits.image.
Just my 2 cents
On Thu, Jan 10, 2013 at 3:21 PM, Lars Buitinck <l.j.buiti...@uva.nl> wrote:
> 2013/1/10 Andreas Mueller <amuel...@ais.uni-bonn.de>:
> > 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.
>
> What we could do is determine a focus for the next release other than
> "add more features", like:
> * implement Python 3 support
> * fix outstanding API issues, like sparse matrix support for some
> estimators.
>
> That would be a step towards a 1.0.
>
> --
> Lars Buitinck
> Scientific programmer, ILPS
> University of Amsterdam
>
>
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