dt = DecisionTree.DecisionTree(
training_datafile=training_datafile,
csv_class_column_index=2,
csv_columns_for_features=[3, 4, 5, 6, 7, 8],
entropy_threshold=0.01,
max_depth_desired=8,
symbolic_to_numeric_cardinality_threshold=10,
)
dt.get_training_data()
dt.calculate_first_order_probabilities()
dt.calculate_class_priors()
# UNCOMMENT THE FOLLOWING LINE if you would like to see the training
# data that was read from the disk file:
#dt.show_training_data()
root_node = dt.construct_decision_tree_classifier()
# UNCOMMENT THE FOLLOWING LINE if you would like to see the decision
# tree displayed in your terminal window:
#print("\n\nThe Decision Tree:\n")
#root_node.display_decision_tree(" ")
# YOu must construct an instance of the DT introspection class and
# initialize the instance before you can get any answers through
# introspection by the instance:
introspector = DecisionTree.DTIntrospection(dt)
introspector.initialize()
introspector.display_training_samples_at_all_nodes_direct_influence_only()
