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()