Github user jkbradley commented on the pull request:
https://github.com/apache/spark/pull/7538#issuecomment-126970026
@MechCoder Just getting to this now, sorry! It looks quite clean. My main
question is how we should handle multiclass LogisticRegression. Any ideas? I
could imagine 2 main options:
* (A) Include the union of MulticlassMetrics and
BinaryClassificationMetrics in the summary. For multiclass data, have
empty/bad values (or exceptions) for binary classification metrics which do not
make sense for multiclass.
* (B) Create a LogisticRegressionSummary which has multiclass metrics.
Also create a BinaryLogisticRegressionSummary which extends
LogisticRegressionSummary and provides binary metrics.
I'd prefer (B). That could be done all in this PR, or it could be split
into 2 (with the first only adding LogisticRegressionSummary, and the second
adding BinaryLogisticRegressionSummary).
CC: @feynmanliang
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