def decision_tree(examples, attributes, bin_targets):
all_same = check_all_same(bin_targets)
if all_same:
return TreeNode(None, True, bin_targets.iloc[0].iloc[0])
elif not attributes:
# Majority Value
return TreeNode(None, True, majority_value(bin_targets))
else:
best_attribute = choose_best_decision_attr(examples, attributes,
bin_targets)
tree = TreeNode(best_attribute)
for vi in range(0, 2):
examples_i = examples.loc[examples[best_attribute] == vi]
indices = examples_i.index.values
bin_targets_i = bin_targets.ix[indices]
if examples_i.empty:
# Majority Value
return TreeNode(None, True, majority_value(bin_targets))
else:
attr = set(attributes)
attr.remove(best_attribute)
tree.set_child(vi,
decision_tree(examples_i, attr, bin_targets_i))
return tree
