Github user jkbradley commented on a diff in the pull request: https://github.com/apache/spark/pull/8734#discussion_r48616278 --- Diff: mllib/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala --- @@ -776,7 +776,7 @@ private[ml] object RandomForest extends Logging { val categoryStats = binAggregates.getImpurityCalculator(nodeFeatureOffset, featureValue) val centroid = if (categoryStats.count != 0) { - categoryStats.predict + categoryStats.calculate() --- End diff -- This should use the soft prediction, not simply the impurity. For regression, that means using predict(), but for binary classification, that will require using categoryStats.stats(1).
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