[MRG+1] Changed the metric average types #6908
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| @@ -469,7 +469,7 @@ def test_precision_recall_f1_score_multiclass_pos_label_none(): | |||
| # compute scores with default labels introspection | |||
| p, r, f, s = precision_recall_fscore_support(y_true, y_pred, | |||
| pos_label=None, | |||
| average='weighted') | |||
| average='binary') | |||
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This one is intentionally testing the case where precision_recall_fscore_support should treat a binary problem as if it's multiclass. So this should be weighted or macro, not binary.
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Otherwise, LGTM... although you should have used a more meaningful commit message. |
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LGTM. |
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LGTM. It annoys me that Appveyor is failing, but I think that it's unrelated, so I am merging. |
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Fixes #6847
Changed the example in
examples/model_selection/grid_search_digits.pyas the dataset is balanced....And same goes for
twenty_newsgroups.rstexample, I cross-checked and the dataset was balanced so I changed the average tomacro. Fixes 2 more files.Being this my first PR to scikit-learn, I'm not sure I did it completely or not, if not I'd be happy to make any other changes as well :)