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From "Peng Meng (JIRA)" <>
Subject [jira] [Commented] (SPARK-17870) ML/MLLIB: Statistics.chiSqTest(RDD) is wrong
Date Tue, 11 Oct 2016 09:49:20 GMT


Peng Meng commented on SPARK-17870:

hi [~srowen], thanks very much for you quickly reply. 
yes,the p-value is better than raw statistic in this case, because p-value is count  based
on DoF and raw statistic.
raw statistic is also popular for feature selection. The SelectKBest and SelectPercentile
in scikit learn is based on raw statistic. 
The question here is we should use the same DoF like scikit learn to count ChiSquare value.

For this JIRA, I propose to change the method to count ChiSquare value like what is done in
scikit learn (change Statistics.chiSqTest(RDD)). 

Thanks very much.  

> ML/MLLIB: Statistics.chiSqTest(RDD) is wrong 
> ---------------------------------------------
>                 Key: SPARK-17870
>                 URL:
>             Project: Spark
>          Issue Type: Bug
>          Components: ML, MLlib
>            Reporter: Peng Meng
>            Priority: Critical
> The method to count ChiSqureTestResult in mllib/feature/ChiSqSelector.scala  (line 233)
is wrong.
> For feature selection method ChiSquareSelector, it is based on the ChiSquareTestResult.statistic
(ChiSqure value) to select the features. It select the features with the largest ChiSqure
value. But the Degree of Freedom (df) of ChiSqure value is different in Statistics.chiSqTest(RDD),
and for different df, you cannot base on ChiSqure value to select features.
> Because of the wrong method to count ChiSquare value, the feature selection results are
> Take the test suite in ml/feature/ChiSqSelectorSuite.scala as an example:
> If use selectKBest to select: the feature 3 will be selected.
> If use selectFpr to select: feature 1 and 2 will be selected. 
> This is strange. 
> I use scikit learn to test the same data with the same parameters. 
> When use selectKBest to select: feature 1 will be selected. 
> When use selectFpr to select: feature 1 and 2 will be selected. 
> This result is make sense. because the df of each feature in scikit learn is the same.
> I plan to submit a PR for this problem.

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