Erik Erlandson created SPARK-15699: -------------------------------------- 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) -- 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