It's not a very clear error message, and PR#2278 corrected that. "The sum
of true positives and false positives" for some label l is the number of
times your system predicts l in the fold's test data. So it's certainly
possible that this will occur, no matter what CV strategy you take. A
stratified CV only ensures that "the sum of true positives and false
negatives" is non-zero.
- Joel
On Fri, Oct 18, 2013 at 1:37 AM, Olivier Grisel <olivier.gri...@ensta.org>wrote:
> 2013/10/17 Josh Wasserstein <ribonucle...@gmail.com>:
> > Hi Joel and others,
> >
> > Sorry, but I am still confused.. If I am using stratified shuffle
> splitting,
> > shouldn't I always have some positives in the testing set (I have
> positives
> > in the full dataset)? The message says: "The sum of true positives and
> false
> > positives (in other words total # of positives in the testing fold) are
> > equal to zero for some labels"
>
> Sounds like a bug, can you try to iterate manually over the CV folds
> and check the labels you get from those train and test fold indices?
>
> Fix the random_state to get reproducible outcomes.
>
> --
> Olivier
>
>
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