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https://issues.apache.org/jira/browse/SPARK-18088?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15605231#comment-15605231
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Peng Meng commented on SPARK-18088:
-----------------------------------
In the previous implementation, testing against only the statistic is not
right.
So I submit https://issues.apache.org/jira/browse/SPARK-17870 to fix that bug.
Testing against only the p-value is ok. 3 of 5 feature selection methods of
sklearn are only based on p-value. The other two is based on statistic. Because
the degree of freedom is the same when compute chiSquare value, so sklearn can
use statistic.
> ChiSqSelector FPR PR cleanups
> -----------------------------
>
> Key: SPARK-18088
> URL: https://issues.apache.org/jira/browse/SPARK-18088
> Project: Spark
> Issue Type: Bug
> Components: ML
> Reporter: Joseph K. Bradley
> Assignee: Joseph K. Bradley
>
> There are several cleanups I'd like to make as a follow-up to the PRs from
> [SPARK-17017]:
> * Rename selectorType values to match corresponding Params
> * Add Since tags where missing
> * a few minor cleanups
> One major item: FPR is not implemented correctly. Testing against only the
> p-value and not the test statistic does not really tell you anything. We
> should follow sklearn, which allows a p-value threshold for any selection
> method:
> [http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectFpr.html]
> * In this PR, I'm just going to remove FPR completely. We can add it back in
> a follow-up PR.
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