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https://issues.apache.org/jira/browse/SPARK-21340?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Hyukjin Kwon updated SPARK-21340:
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Labels: bulk-closed (was: )
> Bring PySpark MLLib evaluation metrics to parity with Scala API
> ---------------------------------------------------------------
>
> Key: SPARK-21340
> URL: https://issues.apache.org/jira/browse/SPARK-21340
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Affects Versions: 2.1.1
> Reporter: Jake Charland
> Priority: Major
> Labels: bulk-closed
>
> This JIRA is a request to bring in PySparks MLLib evaluation metrics to
> parity with the Scala API. For example in BinaryClassificationMetrics there
> are only two eval metrics exposed to pyspark, areaUnderROC and areaUnderPR
> while scala has support for a much wider set of eval metrics including
> precision recall curves and the ability to set thresholds for recall and
> precision values. These evaluation metrics are critical for understanding and
> seeing the performance of trained models and should be available to those
> using the pyspak api's.
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