I put a draft schedule here:
https://github.com/scikit-learn/scikit-learn/wiki/Upcoming-events#technical-discussions-schedule
it's obviously somewhat opinionated ;)
Happy to reprioritize.
Basically I wouldn't like to miss any of the big API discussions because
coming late to the party.
The two things on (grid?) searches are somewhat related: one is about
specifying search-spaces, the other about executing a given search space
efficiently. They probably warrant separate discussions.
I haven't added plotting or sample props on it, which are maybe two
other discussion points.
I tried to cover most controversial things from the roadmap.
Not sure if discussing the schedule via the mailing list is the best
way? Don't want to create even more traffic than I already am ;)
On 2/19/19 5:48 PM, Guillaume Lemaître wrote:
> Not sure if Guillaume had ideas about the schedule, given that he
seems to be running the show?
Mostly running behind the show ...
For the moment, we only have a 30 minutes presentation of introduction
planned on Monday.
For the rest of the week, this is pretty much opened and I think that
we can propose a schedule such that we can be efficient.
IMO, two meetings of an hour per day look good to me.
Shall we prioritize the list of the issues? Maybe that some issues
could be packed together.
I would not be against having a rough schedule on the wiki directly
and I think that having it before Monday could be better.
Let me know how I can help.
On Tue, 19 Feb 2019 at 22:23, Andreas Mueller <t3k...@gmail.com
<mailto:t3k...@gmail.com>> wrote:
Yeah, sounds good.
I didn't want to unilaterally post a schedule, but doing some
google form or similar seems a bit heavy-handed?
Not sure if Guillaume had ideas about the schedule, given that he
seems to be running the show?
On 2/19/19 4:17 PM, Joel Nothman wrote:
I don't think optics requires a large meeting, just a few people.
I'm happy with your proposal generally, Andy. Do we schedule
specific topics at this point?
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--
Guillaume Lemaitre
INRIA Saclay - Parietal team
Center for Data Science Paris-Saclay
https://glemaitre.github.io/
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