anchor.diff:  model
     .stages(5) // <1>
     .asInstanceOf[DecisionTreeClassificationModel] // <2>
  -  .toDebugString() // <3>
  +  .toDebugString // <3>
   ----
   <1> You need to know the index of the decision tree model in the pipeline model
   (it's the same as the index of the decision tree in the pipeline).
  
positive.code: still don't know how a decision tree classifier might arrive at this result (or
  a far better one).
  
  The process of learning, or building the decision tree, can
  be broken down as the following process: finding the best decision to make from
  the current set of observations, deciding if this decision has to be turned into
  a decision node or a leaf node according to a few criteria. If we choose to
  create a decision node, we go back to the "finding the best decision" step until
  we're left with only leaves.
  
  Each best decision corresponds to a binary split of the dataset according to a
  feature value. Intuitively, the best decisions are the ones that most
  
negative.code: 
  - Find the next decision nodes of the trees we built in figure 4.7.
  - Compare your computations for Gini impurities, entropies and information gains
  in from part 2.6.
  - Find the most common labels from the `ghIssues` variable in listing 4.11.
  
  .Answer
  