zhengruifeng created SPARK-13677:
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Summary: Support Tree-Based Feature Transformation for mllib
Key: SPARK-13677
URL: https://issues.apache.org/jira/browse/SPARK-13677
Project: Spark
Issue Type: New Feature
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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