Module description: Decision trees naturally support classification problems and can be modified to handle regression problems. Thus, they can be very useful in data mining activities as well as data classification. A decision tree classifies data items by posing a series of questions about the features associated with the items. Each question is contained in a node, and every internal node points to one child node for each possible answer to its question. The questions thereby form a hierarchy, encoded as a tree. In the simplest form, we ask yes – or – no questions, and each internal node has a ‘yes’ child and a ‘no’ child. An item is sorted into a class by following the path from the topmost node, the root, to a node without children, a leaf, according to the answers that apply to the item under consideration. An item is assigned to the class that has been associated with the leaf it reaches. Decision trees are constructed by analyzing a set of training examples for which the class labels are known (supervised learning). They are then applied to classify previously unseen examples.

Introduction to functionality of decision trees: Kingsford C., Salzberg S. L. What are decision trees. Nature Biotechnology. – v.26, No.9 (2008) pp 1011 – 1013