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_r250227461
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File path: mllib/src/test/scala/org/apache/spark/ml/util/MLTestingUtils.scala
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@@ -268,4 +269,20 @@ object MLTestingUtils extends SparkFunSuite {
assert(newDatasetF.schema(featuresColName).dataType.equals(new
ArrayType(FloatType, false)))
(newDataset, newDatasetD, newDatasetF)
}
+
+ def modelPredictionEquals[M <: PredictionModel[_, M]](
Review comment:
Hm, what about just setting a looser tolerance then? my worry is that this
allows some values to be very wrong if most are right. I am not against this if
it's really the best way, but what would the tolerances have to be to pass just
asserting every value is within tolerance?
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