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

["Parv Oberoi (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-5133?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14496625#comment-14496625 ] 

Parv Oberoi commented on SPARK-5133:
------------------------------------

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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