My understanding is that this is a rather well-grounded and light-weight
sketching technique, that fits well in sklearn.
+1 for me
But yes, I remember that we have enforced the 200-citation rule quite
strictly in the past.
Best,
B
On 26/02/2019 10:46, Gael Varoquaux wrote:
I need core devs opinion (please, only core devs, I am sending this on
the public ML for transparency):
The following PR adds a speed up for expansion of polynomial kernels:
https://github.com/scikit-learn/scikit-learn/pull/13003
According to the author, the speed up is significant (needs to be
verified during a code review).
The paper is a bit below citation level for inclusion of a new method,
however this can be seen as a speed up of Nystrom. Strictly speaking, it
is not just a speed-up, as it introduces a new estimator.
The discussion on the PR is short and quickly reviews the relevant
literature.
My question: should we consider this as acceptable for inclusion
(provided that it does show significant speedups with good prediction
accuracy)? I am asking to know if we start the review and inclusion
process or not.
Cheers,
Gaël
_______________________________________________
scikit-learn mailing list
scikit-learn@python.org
https://mail.python.org/mailman/listinfo/scikit-learn
_______________________________________________
scikit-learn mailing list
scikit-learn@python.org
https://mail.python.org/mailman/listinfo/scikit-learn
