Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/8734#discussion_r50285840
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala ---
@@ -740,7 +740,7 @@ private[ml] object RandomForest extends Logging {
val categoryStats =
binAggregates.getImpurityCalculator(nodeFeatureOffset,
featureValue)
val centroid = if (categoryStats.count != 0) {
- categoryStats.predict
+ categoryStats.prob(categoryStats.predict)
--- End diff --
I don't believe this is correct. Ordering by the probability of the
prediction is essentially the same as ordering by impurity. That's because when
the impurity is low, the predicted value will have high probability and vice
versa.
From Hastie, Tibshirani, and Friedman:
"We order the predictor classes according to the proportion falling in
outcome class 1. Then we split this predictor as if it were an ordered
predictor."
For binary category I think it should be as @jkbradley suggested
`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]
