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From: jkbradley <git@git.apache.org>
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Subject: [GitHub] spark pull request: [SPARK-14264][PYSPARK][ML] Add feature
 importa...
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Message-Id: <20160330194930.D9282DFC11@git1-us-west.apache.org>
Date: Wed, 30 Mar 2016 19:49:30 +0000 (UTC)

Github user jkbradley commented on a diff in the pull request:

    https://github.com/apache/spark/pull/12056#discussion_r57951739
  
    --- Diff: python/pyspark/ml/classification.py ---
    @@ -500,16 +500,12 @@ def featureImportances(self):
             """
             Estimate of the importance of each feature.
     
    -        This generalizes the idea of "Gini" importance to other losses,
    -        following the explanation of Gini importance from "Random Forests" documentation
    -        by Leo Breiman and Adele Cutler, and following the implementation from scikit-learn.
    +        Each feature's importance is the average of its importance across all trees in the ensemble
    +        The importance vector is normalized to sum to 1. This method is suggested by Hastie et al.
    +        (Hastie, Tibshirani, Friedman. "The Elements of Statistical Learning, 2nd Edition." 2001.)
    +        and follows the implementation from scikit-learn.
     
    -        This feature importance is calculated as follows:
    -         - Average over trees:
    -            - importance(feature j) = sum (over nodes which split on feature j) of the gain,
    -              where gain is scaled by the number of instances passing through node
    -            - Normalize importances for tree to sum to 1.
    -         - Normalize feature importance vector to sum to 1.
    +        .. seealso:: :attr:`DecisionTreeClassificationModel.featureImportances`
    --- End diff --
    
    Does this need to be ```:py:attr:```?  (same for other places)


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