Decision-tree-learning algorithm pseudocode

decisionTreeLearning.py

def decisionTreeLearning(examples, attributes, parent_examples):
  if len(examples) == 0:
    return pluralityValue(parent_examples)
    # return most probable answer as there is no training data left
  elif len(attributes) == 0:
    return pluralityValue(examples)
  elif (all examples classify the same):
    return their classification
  
  A = max(attributes, key(a)=importance(a, examples)
  # choose the most promissing attribute to condition on
  tree = new Tree(root=A)
  for value in A.values():
    exs = examples[e.A == value]
    subtree = decisionTreeLearning(exs, attributes.remove(A), examples)
    # note implementation should probably wrap the trivial case returns into trees for consistency
    tree.addSubtreeAsBranch(subtree, label=(A, value)
  
  return tree

Fig. 18.5, page 702

pluralityValue(examples) should select the most probable answer in case there are no more attributes to use or no examples for this end of the three