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https://issues.apache.org/jira/browse/SPARK-5133?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14648565#comment-14648565
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Joseph K. Bradley commented on SPARK-5133:
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Yes, that's the approach I was planning on taking. I realized that this JIRA
is blocked by the one I just linked; I'll try to get it merged ASAP. I
appreciate your offer to contribute it, and I would normally say yes, but I'm a
bit panicked by the code freeze. Since I'm very familiar with the tree code,
would you mind letting me send it, and helping to review my PR once I send one?
If you are interested, it would be great to collaborate to add better methods
of measuring feature importance (or other features) for the next release cycle.
Thanks again!
> Feature Importance for Decision 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
> Original Estimate: 168h
> Remaining Estimate: 168h
>
> 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.
> More information on feature importance (via decrease in impurity) can be
> found in ESLII (10.13.1) or here [1].
> R's randomForest package uses a different technique for assessing variable
> importance that is based on permutation tests.
> All necessary information to create relative importance scores should be
> available in the tree representation (class Node; split, impurity gain,
> (weighted) nr of samples?).
> [1]
> http://scikit-learn.org/stable/modules/ensemble.html#feature-importance-evaluation
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