Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/8734#discussion_r50572835
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala ---
@@ -740,7 +740,11 @@ private[ml] object RandomForest extends Logging {
val categoryStats =
binAggregates.getImpurityCalculator(nodeFeatureOffset,
featureValue)
val centroid = if (categoryStats.count != 0) {
- categoryStats.predict
+ if (categoryStats.count == 2) {
--- End diff --
I think you meant `categoryStats.stats.length == 2`. `categoryStats.count`
is the count of data points falling into that particular bin. Since we are
trying to determine here whether this is regression or binary classification, I
think checking `if (binAggregates.metadata.isClassification)` is more clear.
Additionally, the code under the if and else statements of
`centroidForCategories` is identical except for a single line. It seems cleaner
to restructure to something like:
```scala
val centroidForCategories = Range(0, numCategories).map { case featureValue
=>
val categoryStats =
binAggregates.getImpurityCalculator(nodeFeatureOffset, featureValue)
val centroid = if (categoryStats.count != 0) {
if (binAggregates.metadata.isMulticlass) {
// multiclass classification
categoryStats.calculate()
} else if (binAggregates.metadata.isClassification) {
// binary classification
categoryStats.stats(1)
} else {
// regression
categoryStats.predict
}
} else {
Double.MaxValue
}
(featureValue, centroid)
}
```
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