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  1. Spark
  2. SPARK-15699

Add chi-squared test statistic as a split quality metric for decision trees

    Details

    • Type: New Feature
    • Status: In Progress
    • Priority: Minor
    • Resolution: Unresolved
    • Affects Version/s: 2.0.0
    • Fix Version/s: None
    • Component/s: ML, MLlib
    • Labels:
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      Description

      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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              • Assignee:
                Unassigned
                Reporter:
                eje Erik Erlandson
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                • Created:
                  Updated: