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

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