Decision tree learning is a method for approximating discrete-valued target functions, in which the learned function is represented by a decision tree. Learned trees  can also be re-represented as sets of if-then rules to improve human readability.  These learning methods are among the most popular of inductive inference algo-  rithms and have been successfully applied to a broad range of tasks from learning  to diagnose medical cases to learning to assess credit risk of loan applicants.