[jira] [Resolved] (SPARK-17017) Add a chiSquare Selector based on False Positive Rate (FPR) test

["Sean Owen (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-17017?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Sean Owen resolved SPARK-17017.
-------------------------------
       Resolution: Fixed
    Fix Version/s: 2.1.0

Issue resolved by pull request 14597
[https://github.com/apache/spark/pull/14597]

> Add a chiSquare Selector based on False Positive Rate (FPR) test
> ----------------------------------------------------------------
>
>                 Key: SPARK-17017
>                 URL: https://issues.apache.org/jira/browse/SPARK-17017
>             Project: Spark
>          Issue Type: New Feature
>            Reporter: Peng Meng
>            Priority: Minor
>             Fix For: 2.1.0
>
>   Original Estimate: 24h
>  Remaining Estimate: 24h
>
> Univariate feature selection works by selecting the best features based on univariate statistical tests. False Positive Rate (FPR) is a popular univariate statistical test for feature selection. Is it necessary to add a chiSquare Selector based on False Positive Rate (FPR) test, like it is implemented in scikit-learn. 
> http://scikit-learn.org/stable/modules/feature_selection.html#univariate-feature-selection



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