[ https://issues.apache.org/jira/browse/SPARK-13677?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
zhengruifeng reopened SPARK-13677: ---------------------------------- update the design > 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: ML > 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 model1 : DecisionTreeClassificationModel= ... > model1.setLeafCol("leaves") > model1.transform(df) > val model2 : GBTClassificationModel = ... > model2.transform(df) > > > design doc: > https://docs.google.com/document/d/1d81qS0zfb6vqbt3dn6zFQUmWeh2ymoRALvhzPpTZqvo/edit?usp=sharing -- This message was sent by Atlassian JIRA (v7.6.3#76005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org