Github user imatiach-msft commented on the issue:
https://github.com/apache/spark/pull/16654
@zhengruifeng don't most ML libraries have separate clustering evaluators?
For example, WEKA has ClusterEvalution class. Scikit-learn just has a metrics
class and functions you can call, but I don't really like that option and in
any case it is separate from the estimator/model. H2O has a MetricBuilder that
all ML learners (supervised/unsupervised) generate. I think creating a
separate evaluation class which would fit in with the other evaluators in spark
would be ideal - it would conform to the structure of the current codebase and
possibly limit any confusion users might have.
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