Classification rules represent knowledge in the form of logical if-else statements that assign a class to unlabeled examples. They are specified in terms of an antecedent and a consequent, which form a statement that says "if this happens, then that happens." The antecedent comprises certain combinations of feature values, while the consequent specifies the class value to assign if the rule's conditions are met. A simple rule might state, "if the hard drive is making a clicking sound, then it is about to fail."
Rule learners are a closely related sibling of decision tree learners and are often used for similar types of tasks. Like decision trees, they can be used for applications that generate knowledge for future action, such as:
Identifying conditions that lead to hardware failure in mechanical devices
Describing the key characteristics of groups of people for customer segmentation
Finding conditions that precede large drops or increases in the prices of...
