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From "Jake Charland (JIRA)" <j...@apache.org>
Subject [jira] [Created] (SPARK-21340) Bring PySpark MLLib evaluation metrics to parity with Scala API
Date Fri, 07 Jul 2017 14:19:00 GMT
Jake Charland created SPARK-21340:
-------------------------------------

             Summary: 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


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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