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Posted to issues@spark.apache.org by "Hyukjin Kwon (JIRA)" <ji...@apache.org> on 2019/05/21 04:21:13 UTC
[jira] [Updated] (SPARK-15699) Add chi-squared test statistic as a
split quality metric for decision trees
[ https://issues.apache.org/jira/browse/SPARK-15699?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hyukjin Kwon updated SPARK-15699:
---------------------------------
Labels: bulk-closed (was: )
> 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: New Feature
> Components: ML, MLlib
> Affects Versions: 2.0.0
> Reporter: Erik Erlandson
> Priority: Minor
> Labels: bulk-closed
>
> 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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