A Decision tree is a flow chart-like tree structure, where each internal node (non-leaf node) denotes a test on an attribute, each branch represents an outcome of the test and each leaf node (or terminal node) holds a class label. The topmost node of a tree is the root node.
A Decision tree is a classification scheme that generates a tree and a set of rules, representing the model of different classes, from a given data set. The set of records available for developing classification methods is generally divided into two disjoint subsets namely a training set and a test set. The former is used for originating the classifier while the latter is used to measure the accuracy of the classifier. The accuracy of the classifier is determined by the percentage of the test examples that are correctly classified.
In the decision tree classifier, we categorize the attributes of the records into two different types. Attributes whose domain is numerical are called the numerical attributes and the attributes whose domain is not numerical are called categorical attributes. There is one distinguished attribute called a class label. The goal of classification is to build a concise model that can be used to predict the class of the records whose class label is unknown. Decision trees can simply be converted to classification rules.
