Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/7838#discussion_r36040523
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala ---
@@ -1113,4 +1114,77 @@ private[ml] object RandomForest extends Logging {
}
}
+ /**
+ * Given a Random Forest model, compute 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.
+ *
+ * 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 based on total number of
training instances used
+ * to build tree.
+ * - Normalize feature importance vector to sum to 1.
+ *
+ * Note: This should not be used with Gradient-Boosted Trees. It only
makes sense for
+ * independently trained trees.
+ * Note: This is returned as a Map since models do not store the number
of features.
+ * That should be corrected in the future.
+ * @param trees Unweighted forest of trees
+ * @return Feature importance values. Returned as map from feature
index to importance.
+ */
+ private[ml] def featureImportances(trees: Array[DecisionTreeModel]):
Map[Int, Double] = {
+ val totalImportances = new OpenHashMap[Int, Double]()
+ trees.foreach { tree =>
+ // Aggregate feature importance vector for this tree
+ val importances = new OpenHashMap[Int, Double]()
+ computeFeatureImportance(tree.rootNode, importances)
+ // Normalize importance vector for this tree, and add it to total.
+ val treeNorm = tree.rootNode.impurityStats.count
--- End diff --
@jkbradley Correctly, I agree with you.
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