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

["Erik Erlandson (JIRA)" <ji...@apache.org>]

Erik Erlandson created SPARK-15699:
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             Summary: 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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