Decision Tree

Decision Tree Learning Applet (UBC, V4.0)

Learning is the ability to improve one's behaviour based on experience and represents an essential element of computational intelligence. Decision trees are a simple yet successful technique for supervised classification learning. This applet demonstrates how to build a decision tree using a training data set and then use the tree to classify unseen examples in a test data set.

This applet provides several sample data sets of examples to learn and classify, however, you can also create or import your own data sets. Before building a decision tree, the data set can be viewed, and examples can be moved to and from the training set and test set. The applet's Create Mode allows you to watch as a decision tree is built automatically, or build the tree yourself. When building the tree manually, you can use several tools to gain more information that can guide your decisions. Once the decision tree is built, switch to Test Mode to test the tree against the unseen examples in your test set.

Click on the "Start Applet" button to start the Decision Tree Applet with an empty graph. If you notice any bugs or shortcomings, please check out our Bugs & Enhancements page.

If you are using a WWW browser like Netscape or Internet Explorer, you will not be able to save files. Instead, you must use the "Edit" --> "View/Edit Text Representation of Graph" option and cut and paste between the text window and a text editor on your computer.

This applet seems to run well on Netscape 4.5+ and IE 4+. It has been tested on Windows, Linux, and Solaris platforms.

Java Applet

Quick Start

Creating a Decision Tree:

There are two ways to acquire a data set to build a decision tree for. You can create a new data set and input data examples, or you can load a sample data set. If you wish to create a new data set, see the detailed help section on this topic.

To load a sample data set, click "Load Sample Data Set" from the "File" menu. Then select a data set from the drop-down menu and click "Load." The next step is to view and manipulate the examples in the data set. Click the "View/Edit Examples" button near the top of the left-side control panel. This opens a dialog window that can be used to add examples, remove examples, and transfer examples between the training set (left side) and the test set (right side). Make sure there are several examples in the test set before proceeding to build the decision tree.

Now that you have a data set ready, you can begin building the decision tree automatically by clicking the "Step" button until the tree is complete. Alternatively, you can use the "Split Node" and "Set as Leaf" node options modesfrom the tool bar menu to construct the tree yourself. The other node options modes: "View Node Information," "View Mapped Examples," and "Toggle Histogram" can be used to gain information about the data at a particular node to guide your splitting.

Before constructing a decision tree, you may wish to click the "Show Plot" button to view the changes in training set and test set error as the tree is built.

Testing a Decision Tree:

Once the decision tree is built, select the "Test" tab at the top of the control panel. Now click the "Test" button to see how well your decision tree is able to classify the test examples. This will open a window that shows which examples were correctly predicted, which were incorrectly predicted, and those for which the tree cannot make a prediction. The pie chart at the bottom provides a quick indication of the decision tree's performance.

You can also use the node options modes on the control panel to investigate the nodes individually to see their "Node Information," "Mapped Examples," and toggle a probability distribution view.

More Help

Can be found at: