[jira] [Created] (SPARK-5133) Feature Importance for Tree (Ensembles)

["Peter Prettenhofer (JIRA)" <ji...@apache.org>]

Peter Prettenhofer created SPARK-5133:
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             Summary: Feature Importance for Tree (Ensembles)
                 Key: SPARK-5133
                 URL: https://issues.apache.org/jira/browse/SPARK-5133
             Project: Spark
          Issue Type: New Feature
          Components: ML, MLlib
            Reporter: Peter Prettenhofer
            Priority: Minor


Add feature importance to decision tree model and tree ensemble models.
If people are interested in this feature I could implement it given a mentor (API decisions, etc). Please find a description of the feature below:

Decision trees intrinsically perform feature selection by selecting appropriate split points. This information can be used to assess the relative importance of a feature. 
Relative feature importance gives valuable insight into a decision tree or tree ensemble and can even be used for feature selection.

All necessary information to create relative importance scores should be available in the tree representation (class Node; split, impurity gain, (weighted) nr of samples?).



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