Each object is completely described by a set of attributes and a class label. Attributes can have ordered or unordered values. For example, integer values are ordered whereas Boolean values are not. The concept underlying a data set is the true mapping between the attribute set and the class label. A noise-free training set is one in which all the objects are generated using the underlying concept.
All internal nodes have two or more child nodes.
All non-terminal nodes contain splits , which test the value of a mathematical or logical expression of the attributes. Edges from an internal node T to its children are labelled with distinct outcomes of the test at T . Each leaf node has a class label associated with it. The number of classes is finite. A leaf node t is said to be pure if all the training samples at t belong to the same class.