srowen commented on a change in pull request #21632: [SPARK-19591][ML][MLlib]
Add sample weights to decision trees
URL: https://github.com/apache/spark/pull/21632#discussion_r250229729
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File path: mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala
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@@ -74,6 +74,21 @@ private[ml] trait DecisionTreeParams extends PredictorParams
" child to have fewer than minInstancesPerNode, the split will be
discarded as invalid." +
" Should be >= 1.", ParamValidators.gtEq(1))
+ /**
+ * Minimum fraction of the weighted sample count that each child must have
after split.
+ * If a split causes the fraction of the total weight in the left or right
child to be less than
+ * minWeightFractionPerNode, the split will be discarded as invalid.
+ * Should be in the interval [0.0, 0.5).
+ * (default = 0.0)
+ * @group param
+ */
+ final val minWeightFractionPerNode: DoubleParam = new DoubleParam(this,
+ "minWeightFractionPerNode", "Minimum fraction of the weighted sample count
that each child " +
+ "must have after split. If a split causes the fraction of the total weight
in the left or " +
+ "right child to be less than minWeightFractionPerNode, the split will be
discarded as " +
+ "invalid. Should be in interval [0.0, 0.5)",
+ ParamValidators.inRange(0.0, 0.5, lowerInclusive = true, upperInclusive =
false))
Review comment:
Just a Q, the max is 0.5 because at least one of the two children will have
<= 50% of the samples? And = 0.5 isn't really meaningful because that will
always be true.
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