Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/8734#discussion_r48616278
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala ---
@@ -776,7 +776,7 @@ private[ml] object RandomForest extends Logging {
val categoryStats =
binAggregates.getImpurityCalculator(nodeFeatureOffset,
featureValue)
val centroid = if (categoryStats.count != 0) {
- categoryStats.predict
+ categoryStats.calculate()
--- End diff --
This should use the soft prediction, not simply the impurity. For
regression, that means using predict(), but for binary classification, that
will require using categoryStats.stats(1).
---
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 [email protected] or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
