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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.
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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.
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Create A Data Set Video Tutorial
[ Watch Embedded Video ]
[ Download (AVI, 20MB) ]
Build A Decision Tree Video Tutorial
[ Watch Embedded Video ]
[ Download (AVI, 95MB) ]
Testing A Decision Tree Video Tutorial
[ Watch Embedded Video ]
[ Download (AVI, 95MB) ]
See the help section below for other tips on how to use the applet.
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Requirements:
If you are having problems running the applet, you can go to the Java Help
page to ensure that you have the newest version of Java and it is enabled in your browser. Also check the
Supported Platforms page for more info on Browser and OS compatibility.
Help
Downloads
Bugs & Enhancements
Please visit our feedback page and send us your comments about the applets!
Written by Wesley Coelho,Oxana Chakoula and Nicole Arksey with help from David Poole, Alan Mackworth, and Cristina Conati.
Graph Drawing Toolkit by Shinjiro Sueda.
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Video Tutorial