[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-tabpanelfocusedCommentId=14648565#comment-14648565 ] Joseph K. Bradley commented on SPARK-5133: -- Yes, that's the approach I was planning on taking. I realized that this JIRA is blocked by the one I just linked; I'll try to get it merged ASAP. I appreciate your offer to contribute it, and I would normally say yes, but I'm a bit panicked by the code freeze. Since I'm very familiar with the tree code, would you mind letting me send it, and helping to review my PR once I send one? If you are interested, it would be great to collaborate to add better methods of measuring feature importance (or other features) for the next release cycle. Thanks again! 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-tabpanelfocusedCommentId=14648656#comment-14648656 ] Nam Ma commented on SPARK-5133: --- Definitely, Joseph. Please go ahead sending the PR. There are many things we can collaborate to improve the tree-based methods, such as adding other methods of measuring feature importance, as you said, or adding partial dependence. These are not challenging in terms of implementation/scalability/efficiency, but would help a lot for the usability of the methods. 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-tabpanelfocusedCommentId=14646535#comment-14646535 ] Joseph K. Bradley commented on SPARK-5133: -- That's my hope... I'll try to send a PR later today. 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-tabpanelfocusedCommentId=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-tabpanelfocusedCommentId=14647115#comment-14647115 ] Nam Ma commented on SPARK-5133: --- As far as I know, feature importance in scikit-learn is based on Gini Importance of Leo Breiman (originally given in his 1983's paper). Using the current existing stats information in DecisionTreeModel, it is fairly straightforward to compute it. However, we need to modify the InformationGainStats and the execution of Decision Tree a bit to store number of datapoints at that node during the split. I implemented it a long time ago and found the result quite consistent with what scikit-learn provides. Please correct me if I miss something. 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-tabpanelfocusedCommentId=14643730#comment-14643730 ] Joseph K. Bradley commented on SPARK-5133: -- [~pprett] I'd really like to get this into Spark 1.5, and the code cutoff is at the end of this week. Would you be able to send a PR within ~1 day? If not, I can send one instead. Thanks! 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-tabpanelfocusedCommentId=14614799#comment-14614799 ] Peter Prettenhofer commented on SPARK-5133: --- [~yalamart] For some reason i cannot assign it to me -- [~manishamde] can you help me with that? thanks! 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-tabpanelfocusedCommentId=14614812#comment-14614812 ] Sean Owen commented on SPARK-5133: -- We don't generally Assign JIRAs while they're being worked on. Anyone's free to work on anything, with an encouragement to please try to coordinate with anyone else working on it. (Look for an open PR or comments; there is none linked here though.) (Also, JIRA has a problem now wherein we can't add new Contributors, and unless you're added to that group you can't be Assigned :( ) 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-tabpanelfocusedCommentId=14614682#comment-14614682 ] Peter Prettenhofer commented on SPARK-5133: --- [~yalamart] I'm already working on it -- havent published a PR yet 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-tabpanelfocusedCommentId=14614715#comment-14614715 ] Venkata Vineel commented on SPARK-5133: --- [~peter.prettenhofer] Please assign it to yourself, so that others don't get confused and take it up.Can you help to choose and start working on similar bugs/features that others haven't already submitted PRs for. 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-tabpanelfocusedCommentId=14615678#comment-14615678 ] Joseph K. Bradley commented on SPARK-5133: -- Definitely. Please look at the list of starters linked from [https://issues.apache.org/jira/browse/SPARK-8445]. If a JIRA is not assigned and no one has said they are working on it yet, then please comment to say you'd like to work on it. We're working on finding more starter issues. 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-tabpanelfocusedCommentId=14615528#comment-14615528 ] Venkata Vineel commented on SPARK-5133: --- [~josephkb] If you don't mind, can you please give me a couple of bugs/features to start with. I see some things not assigned to any one, but people are already working on them, so there is no clarity which ones are ready to be picked up. Please consider. 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-tabpanelfocusedCommentId=14615507#comment-14615507 ] Joseph K. Bradley commented on SPARK-5133: -- [~yalamart] I see you're looking into quite a few PRs. It's good to ping people to see the status, but please work on only 1 at a time, especially while getting started. Also, please get familiar with [https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark] as you get started. Thanks! 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-tabpanelfocusedCommentId=14614622#comment-14614622 ] Venkata Vineel commented on SPARK-5133: --- [~peter.prettenhofer] Is this still open and can I work on it ? 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-tabpanelfocusedCommentId=14614620#comment-14614620 ] Venkata Vineel commented on SPARK-5133: --- Can I work on this feature ? Please let me know. I m interested. 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-tabpanelfocusedCommentId=14601385#comment-14601385 ] Peter Prettenhofer commented on SPARK-5133: --- [~josephkb] definitely - will start compiling a PR for feature importance via decrease in impurity. 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-tabpanelfocusedCommentId=14601657#comment-14601657 ] Joseph K. Bradley commented on SPARK-5133: -- Great, thank you! Please ping me on the PR when you submit it. 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-tabpanelfocusedCommentId=14600140#comment-14600140 ] Joseph K. Bradley commented on SPARK-5133: -- It's high time we add this to MLlib, so I'm adding this to the 1.5 roadmap. [~peter.prettenhofer] If you are still interested in this, please feel free to take it. Or if others are interested, please comment on this JIRA. The initial API should be quite simple; I'm imagining a single method returning importance for each feature, modeled after what R or other libraries return. I think we should calculate importance based on the learned model. The permutation test would be nice in the future but would be much more expensive (shuffling data). 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 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-tabpanelfocusedCommentId=14596681#comment-14596681 ] Joseph K. Bradley commented on SPARK-5133: -- Linking related umbrella which discusses adding stats like this to models. 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
[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-tabpanelfocusedCommentId=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
