Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/12056#discussion_r57906908
--- 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 --
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.
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