Hi Artem, hi everybody,
There were two API issues and I think both need thought. The first is the
matrix-like Y which at the moment overlaps semantically with multilabel and
multioutput-multiclass (though I think it could be seen as a form of
multi-target regression…)
The second is the `estimator.metric` which would be a new convention. The
problem here is proxying fit/predict/{set|get}_params calls to the parent, as
Joel noted.
IMHO the first is slightly less scary that the second, but I’m not sure where
we should draw the line.
A few thoughts and questions about your proposal, on top of the excellent
comments the others gave so far:
The matrix-like Y links to a question I had: you say it only has -1, 1s and 0s.
But don’t metric learning methods support more fine-grained (continuous) values
there? Otherwise the expressiveness gain over just having a classification y is
not that big, is it?
Overall the proposal would benefit by including a bit more detail on the metric
learning methods and the relationship/differences/tradeoffs between them.
Would metric learning be useful for regression in any way? My question was
triggered by your saying that it could be used in the KNN classifier, which
made me wonder why not in the regressor. E.g. one could bin the `y`.
Nitpicks:
* what does SWE stand for?
* missing articles: equivalent to (linear) -> equivalent to a (linear), as if
trained kernelized -> as if we trained a kernelized, Core contribution-> The
core contribution, expect integration phase -> expect the integration phase.
* I think ITML skips from review #1 to review #3.
Hope this helps,
Yours,
Vlad
> On 24 Mar 2015, at 20:25, Artem <[email protected]> wrote:
>
> You mean matrix-like y?
>
> Gael said
> > FWIW It'll require some changes to cross-validation routines.
> I'd rather we try not to add new needs and usecases to these before we
> release 1.0. We are already having a hard time covering in a homogeneous
> way all the possible options.
>
> Then Andreas
> 1.2: matrix-like Y should actually be fine with cross-validation. I think it
> would be nice if we could get some benefit by having a classification-like y,
> but I'm not opposed to also allowing matrix Y.
>
> So if we don't want to alter API, I suppose this feature should be postponed
> until 1.0?
>
>
> On Wed, Mar 25, 2015 at 1:44 AM, Olivier Grisel <[email protected]>
> wrote:
> I also share Gael's concerns with respect to extending our API in yet
> another direction at a time where we are trying to focus on ironing
> out consistency issues...
>
> --
> Olivier
>
> ------------------------------------------------------------------------------
> 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
> [email protected]
> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
>
> ------------------------------------------------------------------------------
> 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
> [email protected]
> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
------------------------------------------------------------------------------
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
[email protected]
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
