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https://issues.apache.org/jira/browse/SPARK-15699?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15310499#comment-15310499
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Apache Spark commented on SPARK-15699:
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User 'erikerlandson' has created a pull request for this issue:
https://github.com/apache/spark/pull/13440
> Add chi-squared test statistic as a split quality metric for decision trees
> ---------------------------------------------------------------------------
>
> Key: SPARK-15699
> URL: https://issues.apache.org/jira/browse/SPARK-15699
> Project: Spark
> Issue Type: Improvement
> Components: ML, MLlib
> Affects Versions: 2.0.0
> Reporter: Erik Erlandson
> Priority: Minor
>
> Using test statistics as a measure of decision tree split quality is a useful
> split halting measure that can yield improved model quality. I am proposing
> to add the chi-squared test statistic as a new impurity option (in addition
> to "gini" and "entropy") for classification decision trees and ensembles.
> I wrote a blog post that explains some useful properties of test-statistics
> for measuring split quality, with some example results:
> http://erikerlandson.github.io/blog/2016/05/26/measuring-decision-tree-split-quality-with-test-statistic-p-values/
> (Other test statistics are also possible, for example using the Welch's
> t-test variant for regression trees, but they could be addressed separately)
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