Create decision trees to help you with your work. 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.

This applet provides 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.

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

To load a sample dataset:
1. Click "Load Sample Dataset" from the "File" menu.
2. Select a dataset from the drop-down menu and click "Load."
3. Click the "View/Edit Examples" button near the top of the left-side control panel to view and manipulate the examples in the data set. 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.

Requirements:
* Java

The license of this software is Freeware, you can free download and free use this calculator software.