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

    https://github.com/apache/spark/pull/12056#discussion_r57908437
  
    --- 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
    --- End diff --
    
    Fair enough - though the Scala doc should be updated likewise?
    On Wed, 30 Mar 2016 at 17:21, Seth Hendrickson <[email protected]>
    wrote:
    
    > In python/pyspark/ml/classification.py
    > <https://github.com/apache/spark/pull/12056#discussion_r57906908>:
    >
    > > @@ -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
    >
    > There was some discussion on this here
    > <https://github.com/apache/spark/pull/11961>. Basically, since the random
    > forest and GBT importance is just an average of the importances for single
    > trees, we can just state that here and link to the doc for single trees,
    > which explains how those are computed. Otherwise, we copy/paste the same
    > explanation 6 times.
    >
    > —
    > You are receiving this because you are subscribed to this thread.
    > Reply to this email directly or view it on GitHub
    > 
<https://github.com/apache/spark/pull/12056/files/892d30123a192cd3796892c0f64a5cf2993e1f09#r57906908>
    >



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