A Comprehensive Guide to Decision Tree Learning

Decision Tree is one of the most widely used supervised machine learning algorithm (a dataset which has been labeled) for inductive inference. Decision tree learning is a method for approximating discrete valued target functions in which the function which is learned during the training is represented by a decision tree. The learned tree can also be represented as nested if-else rule to improve human readability. Decision tree learning is used for classification as well as regression is often called as classification tree and regression tree respectively. The term Classification And Regression Tree (CART) analysis is used to refer both the tasks.