Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/8734#discussion_r50601582
--- Diff:
mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala ---
@@ -331,12 +336,62 @@ class DecisionTreeSuite extends SparkFunSuite with
MLlibTestSparkContext {
assert(topNode.impurity !== -1.0)
// set impurity and predict for child nodes
- assert(topNode.leftNode.get.predict.predict === 0.0)
- assert(topNode.rightNode.get.predict.predict === 1.0)
+ if (topNode.leftNode.get.predict.predict === 0.0) {
+ assert(topNode.rightNode.get.predict.predict === 1.0)
+ } else {
+ assert(topNode.leftNode.get.predict.predict === 1.0)
+ assert(topNode.rightNode.get.predict.predict === 0.0)
+ }
assert(topNode.leftNode.get.impurity === 0.0)
assert(topNode.rightNode.get.impurity === 0.0)
}
+ test("Use soft prediction for binary classification with ordered
categorical features") {
--- End diff --
What is the goal of this test? I guessed that the goal would be to find a
case where ordering by hard predictions produces a different (suboptimal) tree
than ordering by soft predictions. However, I did a quick simulation for this
dataset and the results I got were the same either way. Just wanted to clarify.
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