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https://issues.apache.org/jira/browse/SPARK-13677?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15638459#comment-15638459
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zhengruifeng commented on SPARK-13677:
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Since mllib is in maintenance status. If this feature will be included, the
corresponding PR will be updated to focus on ML only.
> 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)
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