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https://issues.apache.org/jira/browse/SPARK-5133?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14496625#comment-14496625
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Parv Oberoi commented on SPARK-5133:
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this would be a really useful feature to have in MLLib Ensemble Tree Training.
Exporting Feature Importance is a highly useful during feature engineering.
> 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
> 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.
> 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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