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From "Apache Spark (JIRA)" <j...@apache.org>
Subject [jira] [Assigned] (SPARK-21340) Bring PySpark MLLib evaluation metrics to parity with Scala API
Date Thu, 13 Jul 2017 14:02:00 GMT

     [ https://issues.apache.org/jira/browse/SPARK-21340?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Apache Spark reassigned SPARK-21340:
------------------------------------

    Assignee:     (was: Apache Spark)

> 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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