MLlib: issue with increasing maximum depth of the decision tree

[Sameer Tilak]

Hi All,My dataset is fairly small -- a CSV file with around half million rows 
and 600 features.  Everything works when I set maximum depth of the decision 
tree to 5 or 6. However, I get this error for larger values of that parameter 
-- For example when I set it to 10. Have others encountered a similar issue? 

14/08/20 10:27:26 INFO TaskSetManager: Serialized task 5.0:390 as 400933 bytes 
in 1 ms14/08/20 10:27:26 WARN TaskSetManager: Lost TID 1194 (task 
5.0:399)14/08/20 10:27:26 WARN TaskSetManager: Loss was due to 
java.lang.ArrayIndexOutOfBoundsExceptionjava.lang.ArrayIndexOutOfBoundsException:
 178   at org.apache.spark.mllib.linalg.DenseVector.apply(Vectors.scala:163)   
at org.apache.spark.mllib.tree.DecisionTree$.findBin$1(DecisionTree.scala:444)  
at 
org.apache.spark.mllib.tree.DecisionTree$.org$apache$spark$mllib$tree$DecisionTree$$findBinsForLevel$1(DecisionTree.scala:529)
       at 
org.apache.spark.mllib.tree.DecisionTree$$anonfun$3.apply(DecisionTree.scala:653)
    at 
org.apache.spark.mllib.tree.DecisionTree$$anonfun$3.apply(DecisionTree.scala:653)
    at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)  at 
scala.collection.Iterator$class.foreach(Iterator.scala:727)  at 
scala.collection.AbstractIterator.foreach(Iterator.scala:1157)       at 
scala.collection.TraversableOnce$class.foldLeft(TraversableOnce.scala:144)   at 
scala.collection.AbstractIterator.foldLeft(Iterator.scala:1157)      at 
scala.collection.TraversableOnce$class.aggregate(TraversableOnce.scala:201)  at 
scala.collection.AbstractIterator.aggregate(Iterator.scala:1157)     at 
org.apache.spark.rdd.RDD$$anonfun$21.apply(RDD.scala:838)    at 
org.apache.spark.rdd.RDD$$anonfun$21.apply(RDD.scala:838)    at 
org.apache.spark.SparkContext$$anonfun$23.apply(SparkContext.scala:1116)     at 
org.apache.spark.SparkContext$$anonfun$23.apply(SparkContext.scala:1116)     at 
org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:111)  at 
org.apache.spark.scheduler.Task.run(Task.scala:51)   at 
org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:187)        at 
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) 
     at 
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) 
     at java.lang.Thread.run(Thread.java:744)