def create_node(self, data_set, max_depth, feature_index_list, boost=False):
"""
Recursive function that constructs the decision tree by DFS approach by setting respective parameters
and branching based on information gain and entropy.
:param data_set:
:param max_depth:
:param feature_index_list:
:return:
"""
# tree_node = None
is_termination_condition, class_label = self.test_termination_condition(data_set,
max_depth, feature_index_list)
tree_node = TreeNode()
eval_util = EvalUtil()
data_util = DataUtils()
feature_split_index = eval_util.get_split_attribute_index(data_set, feature_index_list, boost)
# print feature_split_index
tree_node.set_split_feature_index(feature_split_index)
split_feature_values = data_util.get_feature_discrete_values(data_set, feature_split_index)
# tree_node.set_pos_neg(positive, negative)
tree_node.set_class_label(class_label)
revised_index_list = [x for x in feature_index_list if x != feature_split_index]
if not is_termination_condition:
for value in split_feature_values:
data_subset = data_util.get_data_subset(data_set, feature_split_index, value)
if len(data_subset) > 0:
child_node = self.create_node(data_subset, max_depth - 1, revised_index_list)
tree_node.append_child(value, child_node)
else:
tree_node.append_child(value, None)
return tree_node
