Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/12056#discussion_r57953241
--- 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 --
I tried both locally (with/without), and they both seem to work. But I have
seen the `:py:` prefix in other places so I updated it.
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