[scikit-learn] Micro average in classification report

[Joel Nothman]

A lot of this is discussed in
http://scikit-learn.org/dev/modules/model_evaluation.html

If you passed only a limited set of labels in,  micro average would not
necessarily be identical across P/R/F. This allows for a "negative label",
often an experimentally uninteresting majority class.
Try classification_report(y_true, y_pred, target_names=target_names,
labels=[1, 2])

If you passed in multilabel data, micro average would not necessarily be
identical across P/R/F. Try classification_report(np.array([[1, 0], [0,
1]]), np.array([[1, 1], [0, 1]])).

Perhaps for multiclass with labels=None, we could report this differently.
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