Github user XXXShao commented on a diff in the pull request: https://github.com/apache/spark/pull/16722#discussion_r137897201 --- Diff: mllib/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala --- @@ -1002,9 +1018,9 @@ private[spark] object RandomForest extends Logging { val numSplits = metadata.numSplits(featureIndex) // get count for each distinct value - val (valueCountMap, numSamples) = featureSamples.foldLeft((Map.empty[Double, Int], 0)) { - case ((m, cnt), x) => - (m + ((x, m.getOrElse(x, 0) + 1)), cnt + 1) + val (valueCountMap, numSamples) = featureSamples.foldLeft((Map.empty[Double, Double], 0.0)) { --- End diff -- Hi, thanks for your contribution~ I have a question about considering weight info in findSplitsForContinuousFeature here. It looks the continuous features will be influenced much more by instance weight because the weight part is considered twice: (1)make split (2) calculate impurity. Normally weight is only mentioned in impurity calculation part according to limited papers I have read. Could you provide some reference you refer here? And correct me if I misunderstand your code. :) Thanks!
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