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https://issues.apache.org/jira/browse/SPARK-17870?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15565238#comment-15565238
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Sean Owen commented on SPARK-17870:
-----------------------------------
I don't quite understand this example, can you point me to the source? the
chi-squared statistic is indeed a function of observed and expected counts, but
I'd expect those to be a vector of counts, one for each class. If you're saying
that each row contains observed counts for one feature's classes, then yes in
this particular construction each of them has the same number of classes
(columns). But that isn't generally true; that can't be an assumption scikit
makes? I bet I'm missing something.
> ML/MLLIB: Statistics.chiSqTest(RDD) is wrong
> ---------------------------------------------
>
> Key: SPARK-17870
> URL: https://issues.apache.org/jira/browse/SPARK-17870
> 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 strange.
> 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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