Also see https://github.com/scikit-learn/scikit-learn/issues/14268
which is discussing how to make things faster *and* more stable!
On 3/30/20 10:30 AM, Andreas Mueller wrote:
On 3/27/20 6:20 PM, Gael Varoquaux wrote:
Thanks for the link Andy. This is indeed very interesting!
On Fri, Mar 27
On 3/27/20 6:20 PM, Gael Varoquaux wrote:
Thanks for the link Andy. This is indeed very interesting!
On Fri, Mar 27, 2020 at 06:10:28PM +0100, Roman Yurchak wrote:
Regarding learners, Top-5 in both GH17 and GH19 are LogisticRegression,
MultinomialNB, SVC, LinearRegression, and RandomForestCl
Thanks for the link Andy. This is indeed very interesting!
On Fri, Mar 27, 2020 at 06:10:28PM +0100, Roman Yurchak wrote:
> > Regarding learners, Top-5 in both GH17 and GH19 are LogisticRegression,
> > MultinomialNB, SVC, LinearRegression, and RandomForestClassifier (in this
> > order).
> Maybe L
Very interesting! A few comments,
> From GH17, we managed to extract only 10.5k pipelines. The
relatively low frequency (with respect to the number of notebooks using
SCIKIT-LEARN [..]) indicates a non-wide adoption of this specification.
However, the number of pipelines in the GH19 corpus is
Hey all.
There's a pretty cool paper by a team at MS that analyses public github
repos for their use of the sklearn and related libraries:
https://arxiv.org/abs/1912.09536
Thought it might be of interest.
Cheers,
Andy
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