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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