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_r250235678
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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:
I'm wondering about using this method at all. If the problem is that some
values might vary moderately from the expected value, can you just set a wide
tolerance? why does this method need to merely assert that some values are in
tolerance and let some be out of tolerance?
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