DecisionTree 3.2.1

A Python module for decision-tree based classification of multidimensional data

Version 3.2.1 has a bugfix that was needed in one of the probability calculating functions.

Version 3.2.0 adds boosting capability to the decision tree module.

Version 3.0 adds bagging capability to the decision tree module. If you have a large enough training dataset, you can now construct multiple decision trees and have the final classification be based on a majority vote from all the trees. This can average out the noise in the classification process.

Version 2.3 gives the module a new capability — ability to introspect about the classification decisions at the nodes of the decision tree.

With regard to the purpose of the module, assuming you have placed your training data in a CSV file, all you have to do is to supply the name of the file to this module and it does the rest for you without much effort on your part for classifying a new data sample. A decision tree classifier consists of feature tests that are arranged in the form of a tree. The feature test associated with the root node is one that can be expected to maximally disambiguate the different possible class labels for a new data record. From the root node hangs a child node for each possible outcome of the feature test at the root. This maximal class-label disambiguation rule is applied at the child nodes recursively until you reach the leaf nodes. A leaf node may correspond either to the maximum depth desired for the decision tree or to the case when there is nothing further to gain by a feature test at the node.

Typical usage syntax:

training_datafile = "stage3cancer.csv"
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()
  dt.show_training_data()
  root_node = dt.construct_decision_tree_classifier()
  root_node.display_decision_tree("   ")

  test_sample  = ['g2 = 4.2',
                  'grade = 2.3',
                  'gleason = 4',
                  'eet = 1.7',
                  'age = 55.0',
                  'ploidy = diploid']
  classification = dt.classify(root_node, test_sample)
  print "Classification: ", classification