Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/13390#discussion_r65011652
--- Diff: docs/mllib-evaluation-metrics.md ---
@@ -136,6 +136,7 @@ for all other classes. So, a true positive occurs
whenever the prediction and th
occurs when neither the prediction nor the label take on the value of a
given class. By this convention, there can be
multiple true negatives for a given data sample. The extension of false
negatives and false positives from the former
definitions of positive and negative labels is straightforward.
+Note that the [micro
averaged](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html)
accuracy, precision, recall, and F1-measure are the same.
--- End diff --
I think I might leave this out. Actually, overall precision/recall/F1 for
multiclass are deprecated anyway.
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