Decision Trees

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 dataset and then use the tree to classify unseen examples in a test dataset.

Decision Trees is a simple, easy to use application specially designed to provide several sample datasets of examples to learn and classify, however, you can also create or import your own datasets. Before building a decision tree, the dataset 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 view and manipulate the dataset. In Solve Mode, you can 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, you can test the tree against the unseen examples in your test set.