Data classification is a two-step process as training and prediction. In training step, a classifier model is created by analyzing data objects and their class labels. In a prediction step, unknown class labels of data objects are predicted using the generated classifier model. Decision tree induction is one of the mostly known classification techniques. Thus, it falls under the category of predictive data mining tasks. In training step, it generates a decision tree based on class labels of trained data objects. In a decision tree, each node represents a test on an attribute of data, each edge of that node represents a value for that attribute and each leaf represents a class label. Generating a decision tree is generally as a two-step process as tree construction and tree pruning. Therefore, the correct option is E.