Github user erikerlandson commented on a diff in the pull request:
https://github.com/apache/spark/pull/13440#discussion_r104822632
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala
---
@@ -237,6 +237,41 @@ class DecisionTreeClassifierSuite
compareAPIs(rdd, dt, categoricalFeatures = Map.empty[Int, Int],
numClasses)
}
+ test("split quality using chi-squared and minimum gain") {
+ // Generate a data set where the 1st feature is useful and the others
are noise
+ val features = Vector.fill(200) {
+ Array.fill(3) { scala.util.Random.nextInt(2).toDouble }
+ }
+ val labels = features.map { fv =>
+ LabeledPoint(if (fv(0) == 1.0) 1.0 else 0.0, Vectors.dense(fv))
+ }
+ val rdd = sc.parallelize(labels)
+
+ // two-class learning problem
+ val numClasses = 2
+ // all binary features
+ val catFeatures = Map(Vector.tabulate(features.head.length) { j => (j,
2) } : _*)
+
+ // Chi-squared split quality with a p-value threshold of 0.01 should
allow
+ // only the first feature to be used since the others are uncorrelated
noise
+ val train: DataFrame = TreeTests.setMetadata(rdd, catFeatures,
numClasses)
+ val dt = new DecisionTreeClassifier()
+ .setImpurity("chisquared")
+ .setMaxDepth(5)
+ .setMinInfoGain(0.01)
+ val treeModel = dt.fit(train)
+
+ // The tree should use exactly one of the 3 features: featue(0)
--- End diff --
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