Github user sethah commented on a diff in the pull request: https://github.com/apache/spark/pull/20632#discussion_r169833178 --- Diff: mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala --- @@ -303,26 +303,6 @@ class DecisionTreeSuite extends SparkFunSuite with MLlibTestSparkContext { assert(split.threshold < 2020) } - test("Multiclass classification stump with 10-ary (ordered) categorical features") { --- End diff -- Regarding this test - it fails now for a silly reason. Because of the data, the tree built winds up with a right node with equal labels of 1.0 and 2.0. It breaks the tie by prediction 1.0, which left node also predicts. You can modify the data generating method to: ```scala def generateCategoricalDataPointsForMulticlassForOrderedFeatures(): Array[LabeledPoint] = { val arr = new Array[LabeledPoint](3000) for (i <- 0 until 3000) { if (i < 1001) { arr(i) = new LabeledPoint(2.0, Vectors.dense(2.0, 2.0)) } else if (i < 2000) { arr(i) = new LabeledPoint(1.0, Vectors.dense(1.0, 2.0)) } else { arr(i) = new LabeledPoint(1.0, Vectors.dense(2.0, 2.0)) } } arr } ``` so that 2.0 will be predicted. I slightly prefer this, assuming all other tests pass (I checked some of the suites). The less stuff we can move around that is mostly unrelated to this change, the better.
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