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)`
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