Can you give an example?
I imagine that just supporting the data structure will not give you any
speed benefit unless the algorithms are reimplemented to take advantage
of the problem structure.
Even if the output of logistic regression would be a sparse binary
vector, you'd still need to comp
We had one done in 2013 (wow!).
I'll post the link to the internal mailing list since it could have
identifying information.
Obviously the answers now would be quite different, just thought it
would be interesting to look at it again.
On 7/23/19 10:28 AM, Tom Augspurger wrote:
Pandas will be r
If I could pitch in, it would be lovely, very lovely indeed, if
scikit-learn models could:
- operate on sparse data, both input and output by default
- implement some kind of sparse vector representation (as in
https://github.com/scikit-learn/scikit-learn/issues/8908 )
- perhaps have a unifiying n
Pandas will be running one soon too:
https://github.com/pandas-dev/pandas/issues/27477
It may be worth coordinating on questions so that we can compare
communities (or combining surveys to reduce "survey-fatigue" somehow?
Haven't thought through this).
Tom
On Tue, Jul 23, 2019 at 6:54 AM Adrin
It may be worth doing a user survey to get a feeling of what people care
about, we may or may not take them into account afterwards.
Here's how Dask is doing it: https://github.com/dask/dask/issues/4748
On Sun, Jul 14, 2019 at 8:44 PM Andreas Mueller wrote:
> Hi all.
> At SciPy, Brian Granger r
Hi all.
At SciPy, Brian Granger raised a good point about their planning for the
Jupyter Project, which is the importance of long-term goals.
I think it's great that we now have a detailed short-term roadmap
(https://scikit-learn.org/dev/roadmap.html).
Given that we now have about 6(!) full ti
