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 at 18:52, Michael Capizzi <mcapi...@email.arizona.edu> wrote: > 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 this test: > > File > "/Users/mcapizzi/miniconda3/envs/nb_plus_svm/lib/python3.6/site-packages/sklearn/utils/estimator_checks.py", > line 1106, in check_classifiers_train > assert_greater(accuracy_score(y, y_pred), 0.83) > AssertionError: 0.31333333333333335 not greater than 0.83 > > And while it’s disturbing that my classifier is getting 31% accuracy > when, clearly, the test writer expects it to be in the upper-80s, I’m not > sure I understand why that would be a test condition. > > Thanks for any insight. > > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > > -- Guillaume Lemaitre INRIA Saclay - Parietal team Center for Data Science Paris-Saclay https://glemaitre.github.io/
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