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Apache Spark commented on SPARK-15699: -------------------------------------- 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) -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org