Github user MechCoder commented on a diff in the pull request:
https://github.com/apache/spark/pull/13650#discussion_r69039559
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala
---
@@ -105,6 +108,55 @@ class RandomForestRegressorSuite extends SparkFunSuite
with MLlibTestSparkContex
}
}
+ test("Random Forest variance") {
--- End diff --
The first test is meant to pass for all impurities, since it compares the
variance of a forest with one tree (with bootstrapping set off). You are right,
that we have to be deterministic about checking the predicted variances. I have
done it for the DecisionTrees here (https://github.com/apache/spark/pull/13981)
but not sure it is straightforward for a RandomForest....
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastruct...@apache.org or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org
