I have a mlib model:
val model = DecisionTree.train(parsedData, Regression, Variance, maxDepth)
I see model has following methods:algo asInstanceOf isInstanceOf
predict toString topNode
model.topNode outputs:org.apache.spark.mllib.tree.model.Node = id = 0, isLeaf =
false, predict = 0.5, split = Some(Feature = 87, threshold =
0.7931471805599453, featureType = Continuous, categories = List()), stats =
Some(gain = 0.893333, impurity = 0.350000, left impurity = 0.122222, right
impurity = 0.000000, predict = 0.500000)
I was wondering what is the best way to look at the model. We want to see what
the decision tree looks like-- which features are selected, the details of
splitting, what is the depth etc. Is there an easy way to see that? I can
traverse it recursively using topNode.leftNode and topNode.rightNode. However,
was wondering if there is any way to look at the model and also to save it on
the hdfs for later use.
