A pure-Python implementation for constructing a decision tree from multidimensional training data and for using the decision tree for classifying unlabeled data
This module is a pure-Python implementation for constructing a decision tree from multidimensional training data and subsequently using the decision tree to classify future data.
Version 1.5 should work with both Python 3.x and Python 2.x.
Assuming you have arranged your training data in the form of a table in a text file, all you have to do is to supply the name of the training datafile to this module and it does the rest for you without much effort on your part.
A decision tree classifier consists of feature tests that are arranged in the form of a tree. You associate with the root node a feature test that can be expected to maximally disambiguate the different possible class labels for an unlabeled data vector. You then hang from the root node a set of child nodes, one for each value of the feature that you chose for the root node. At each such child node, you now select a feature test that is the most class discriminative given that you have already applied the feature test at the root node and observed the value for that feature. This process is continued until you reach the leaf nodes of the tree. The leaf nodes may either correspond to the maximum depth desired for the decision tree or to the case when you run out of features to test.
Typical usage syntax:
dt = DecisionTree( training_datafile = “training.dat”, debug1 = 1 )
root_node = dt.construct_decision_tree_classifier()
- test_sample = [‘exercising=>never’,
- ‘smoking=>heavy’, ‘fatIntake=>heavy’, ‘videoAddiction=>heavy’]
classification = dt.classify(root_node, test_sample)
print “Classification: “, classification