[
https://issues.apache.org/jira/browse/SPARK-13677?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15796748#comment-15796748
]
Joseph K. Bradley commented on SPARK-13677:
-------------------------------------------
[~podongfeng] Apologies for the inaction on this, but I agree with you about
redoing this for the DataFrame-based API. Could you please propose an API here
before implementing it? Thanks!
> Support Tree-Based Feature Transformation for ML
> ------------------------------------------------
>
> Key: SPARK-13677
> URL: https://issues.apache.org/jira/browse/SPARK-13677
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Reporter: zhengruifeng
> Priority: Minor
>
> It would be nice to be able to use RF and GBT for feature transformation:
> First fit an ensemble of trees (like RF, GBT or other TreeEnsambleModels) on
> the training set. Then each leaf of each tree in the ensemble is assigned a
> fixed arbitrary feature index in a new feature space. These leaf indices are
> then encoded in a one-hot fashion.
> This method was first introduced by
> facebook(http://www.herbrich.me/papers/adclicksfacebook.pdf), and is
> implemented in two famous library:
> sklearn
> (http://scikit-learn.org/stable/auto_examples/ensemble/plot_feature_transformation.html#example-ensemble-plot-feature-transformation-py)
> xgboost
> (https://github.com/dmlc/xgboost/blob/master/demo/guide-python/predict_leaf_indices.py)
> I have implement it in mllib:
> val features : RDD[Vector] = ...
> val model1 : RandomForestModel = ...
> val transformed1 : RDD[Vector] = model1.leaf(features)
> val model2 : GradientBoostedTreesModel = ...
> val transformed2 : RDD[Vector] = model2.leaf(features)
--
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
