Hi!
It looks like you are using the old `sklearn.cross_validation`'s
LeaveOneLabelOut cross-validator. It has been deprecated since v0.18.
Use the `LeaveOneLabelOut` from `sklearn.model_selection`, that should fix
your issue I think (thought I have not looked into your code in detail).
HTH!
On
I see now. So I'll proceed with adding documentation and unit tests for
those kernels to complete their support.
And I don't think they're too specialized, given that many kinds of
feature vectors in e.g. computer vision are in fact histograms and all
of those kernels are histogram-oriented.
A
Dear scikit experts,
I'm struggling with the implementation of a nested cross validation.
My data: I have 26 subjects (13 per class) x 6670 features. I used a feature
reduction algorithm (you may have heard about Boruta) to reduce the
dimensionality of my data. Problems start now: I defined LOS
>
> Okay so in the project, instead of sorting them by Issues / PR why don't
>>> we make one column per priority. Let's have 3 levels and one column for
>>> Done. We have a label for "Stalled" / "Need Contributor" which shows up in
>>> the cards of the project anyway...
>>>
>>> As I didn't want to
I think you get a better view of the importance of Markov Clustering in
academia from
https://scholar.google.co.uk/scholar?hl=en&as_sdt=0,5&q=Markov+clustering .
Raphael
On Sat, 3 Dec 2016 at 22:43 Allan Visochek wrote:
> Thanks for pointing that out, I sort of picked it up by word of mouth so
