GitHub user erikerlandson opened a pull request:
https://github.com/apache/spark/pull/13438
Implement a Chi-Squared test statistic option for measuring split quality
## What changes were proposed in this pull request?
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.
https://issues.apache.org/jira/browse/SPARK-15699
http://erikerlandson.github.io/blog/2016/05/26/measuring-decision-tree-split-quality-with-test-statistic-p-values/
## How was this patch tested?
I added unit testing to verify that the chi-squared "impurity" measure
functions as expected when used for decision tree training.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/erikerlandson/spark pval_split_quality
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/13438.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #13438
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commit b7a47e03c34bb620f751b2816b592acd4392a4bc
Author: Erik Erlandson <[email protected]>
Date: 2016-05-31T21:44:22Z
Implement a Chi-Squared test statistic option for measuring split quality
when training decision trees
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