Each branch contains a set of attributes, or classification rules, that are associated with a particular class label, which is found at the end of the branch.
This type of tree is also known as a classification tree. In these decision trees, nodes represent data rather than decisions. Known as decision tree learning, this method takes into account observations about an item to predict that item’s value. A decision tree can also be used to help build automated predictive models, which have applications in machine learning, data mining, and statistics.
