Sorry for the misinformation.
Yes, actually I'd argue you should raise an error on data that's not
non-negative, if that's not valid input.
Right now there is no way to specify to the testing suite that your
model requires positive data, that's what the PR is about
(among other things) that I r
So it appears that the test check_classifiers_train() (
https://github.com/scikit-learn/scikit-learn/blob/ef5cb84a/sklearn/utils/estimator_checks.py#L1079)
does *not* use the iris dataset after all:
X_m, y_m = make_blobs(n_samples=300, random_state=0)
X_m, y_m = shuffle(X_m, y_m, random_state=7)
X
Thanks @andreas, for your comments, especially the info that it's the
`iris` dataset. I have to dig a bit deeper to see what's going on with the
performance there. But now that I know it's `iris`, I can try to recreate.
-M
On Thu, Oct 12, 2017 at 12:01 AM, Andreas Mueller wrote:
> Yes, it's p
Yes, it's pretty empirical, and with the estimator tags PR
(https://github.com/scikit-learn/scikit-learn/pull/8022) we will be able
to relax it if there's a good reason you're not passing.
But the dataset is pretty trivial (iris), and you're getting chance
performance (it's a balanced three clas
Not sure 100% but this is an integration/sanity check since all classifiers
are supposed to predict quite well and data used to train.
This is true that 83% is empirical but it allows to spot any changes done
in the algorithms even if the unit tests are passing for some reason.
On 11 October 2017
I’m wondering if anyone can identify the purpose of this test:
check_classifiers_train(), specifically this line:
https://github.com/scikit-learn/scikit-learn/blob/ef5cb84a/sklearn/utils/estimator_checks.py#L1106
My custom classifier (which I’m hoping to submit to scikit-learn-contrib)
is failing
