[jira] [Commented] (SPARK-5133) Feature Importance for Decision Tree (Ensembles)
[ https://issues.apache.org/jira/browse/SPARK-5133?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14646493#comment-14646493 ] Parv Oberoi commented on SPARK-5133: [~josephkb]: is the plan to still include this in SPARK 1.5 considering that the code cutoff is this week? > 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 -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-5133) Feature Importance for Decision Tree (Ensembles)
[ 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 -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
