Github user xiejuncs commented on the pull request:
https://github.com/apache/spark/pull/1155#issuecomment-46772612
Nice work.
I am reading the implementation of MulticlassMetrics. According to your
code, for Micro average, you calculate the recall and then let precision and f1
measure equal to the recall. I am not sure whether this makes sense.
According to this post:
http://rushdishams.blogspot.com/2011/08/micro-and-macro-average-of-precision.html
Assume we just have three classes. For each class, we have three numbers,
true positive(tp), false positive(fp), false negative(fn). Hence, we have tp1,
fp1 and fn1 for class 1. so on so forth.
For Micro-Average Precision: (tp1 + tp2 + tp3) / (tp1 + tp2 + tp3 + fp1 +
fp2 + fp3)
For Micro-Average Recall: (tp1 + tp2 + tp3) / (tp1 + tp2 + tp3 + fn1 + fn2
+ fn3)
For Micro-Average F1Measure: it is just the harmonic mean of precision and
recall.
Based on the above definition, recall and precision should not be the same.
Is it correct?
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