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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