Hi Brian.
How about mondrian forests? ;)
And I think Gilles has thought about parallelizing trees a bit.
It's definitely something that people are interested in.
Andy
On 03/06/2017 06:46 AM, Brian Holt wrote:
Thanks Andy,
That's really interesting and gives some hints for future direction.
As an initial suggestion, I wonder if incremental decision tree
learning would be welcomed by the project? My personal experience
building trees was very often frustrated by memory constraints and an
alternative that uses batches would allow the technique to scale up to
much larger datasets that don't fit in memory.
Regards
Brian
On 5 March 2017 at 17:47, Andreas Mueller <[email protected]
<mailto:[email protected]>> wrote:
Hey all.
In case you're interested, here is a summary view of the
scikit-learn survey I posted recently:
https://www.surveymonkey.com/results/SM-RHGZVZ73/
<https://www.surveymonkey.com/results/SM-RHGZVZ73/>
tldr;
Preprocessing takes the most time, people want out-of-core
learning, better integration with pandas
and easier visualization of models and data.
People would use automatic machine learning if it was there, but
it's not the highest priority item.
There is also a lot of interesting info in the comments, but
because I was not able to go through all of them yet,
I don't want to publish them publicly in case there is sensitive
information included (and if anyone knows if there are
legal implications if there wasn't a disclaimer, please let me know).
Cheers,
Andy
_______________________________________________
scikit-learn mailing list
[email protected] <mailto:[email protected]>
https://mail.python.org/mailman/listinfo/scikit-learn
<https://mail.python.org/mailman/listinfo/scikit-learn>
_______________________________________________
scikit-learn mailing list
[email protected]
https://mail.python.org/mailman/listinfo/scikit-learn
_______________________________________________
scikit-learn mailing list
[email protected]
https://mail.python.org/mailman/listinfo/scikit-learn
