No it is the macro average of the per-class f1, i.e. an arithmetic mean over harmonic means of P & R per class
On Fri., 29 Mar. 2019, 9:53 am Max Halford, <maxhalfor...@gmail.com> wrote: > Hey everyone, > > I've stumbled upon an inconsistency with the F1 score and I can't seem to > get around it. I have two lists y_true = [0, 1, 2, 2, 2] and y_pred = [0, > 0, 2, 2, 1]. sklearn tells me that the macro-averaged F1 score is > 0.488888... If I understand correctly the macro-average F1 score is the > harmonic mean of the macro-average precision score and the macro-average > recall score. sklearn tells me that the macro-average precision is 0.5 > whilst the macro-average recall is 0.555555... If use the > statistics.harmonic_mean function from Python's standard library this gives > me around 0.526315. > > So which is correct: 0.488888 or 0.526315? I apologize in advance if I've > overlooked something silly. > > Best regards. > > -- > Max Halford > +336 28 25 13 38 > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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