[jira] [Assigned] (SPARK-15699) Add chi-squared test statistic as a split quality metric for decision trees

["Apache Spark (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-15699?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Apache Spark reassigned SPARK-15699:
------------------------------------

    Assignee:     (was: Apache Spark)

> 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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