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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