Github user zhengruifeng commented on the issue:
https://github.com/apache/spark/pull/16654
@srowen I agree that metric should be irrelevant to details of the
algorithms. AUC is irrelevant to algorithms, it is just relevant to the
dataset: In spark-ml, scikit-learn, or any other packages, the input dataset
contains `label,decision values(or probabilities)`ï¼ if and only if there
exist two labels in the dataset, AUC can be computed, no matter which
classifier is used.
I also agree that some general metrics should be abstracted in Evaluator.
I just disagree that if we treat WSSSE as a general metric:
There have been some attempts to add K-Medoids in spark, although their PRs
were not accepted, there are still some third-party source implementing
K-Medoids on spark.
More realisticly, Spark is used together with other ml-packages in many
cases, suppose use other packages to generate the model locally, and evaluate
the result in spark.
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