Implement a Chi-Squared test statistic option for measuring split quality #13438
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…lity when training decision trees
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Test build #59737 has finished for PR 13438 at commit
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Please follow https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark and update the title |
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nuts, I'm going to have to re-submit a PR against master |
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I'm closing this, re-submitted as #13440 |
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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.