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Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2015/07/31 22:00:04 UTC
[jira] [Commented] (SPARK-5133) Feature Importance for Random
Forests
[ https://issues.apache.org/jira/browse/SPARK-5133?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14649739#comment-14649739 ]
Apache Spark commented on SPARK-5133:
-------------------------------------
User 'jkbradley' has created a pull request for this issue:
https://github.com/apache/spark/pull/7838
> Feature Importance for Random Forests
> -------------------------------------
>
> Key: SPARK-5133
> URL: https://issues.apache.org/jira/browse/SPARK-5133
> Project: Spark
> Issue Type: New Feature
> Components: ML, MLlib
> Reporter: Peter Prettenhofer
> Assignee: Joseph K. Bradley
> Original Estimate: 168h
> Remaining Estimate: 168h
>
> Add feature importance to random forest 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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