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.