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I know there is f1_score metric to get all types of F1 scores (micro, macro, and weighted); however, I would like to be able to print micro averaged F1 score using classification_report of scikit-learn. By default, it seems to be returning weighted micro averaged F1. But I would like the micro averaged F1 in the classification_report. How do I do that?

Also, I know the difference in the formula between weighted and microaverages, but what are the instances where one would be preferred over other? And, what information do they convey?

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    $\begingroup$ The classification_report function isn't very configurable.. usually I just write my own functions for printing classification results - call the appropiate metric functions from sklearn.metrics and print their results.. $\endgroup$
    – stmax
    Commented Jul 15, 2016 at 9:13

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Scikit-learn's classification report has micro averaged F1 score:

Micro average (averaging the total true positives, false negatives and false positives) is only shown for multi-label or multi-class with a subset of classes, because it corresponds to accuracy otherwise and would be the same for all metrics

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