Decision Tree Learning
Decision Tree Learning uses past observations to learn how to classify them and also try to predict the class of a new observation. For example, in a bank, we may have historical information on the granting of loans. Usually, past loan information includes a customer profile and whether the customer defaulted or not. Based on this information, the algorithm can learn to predict whether a new customer will default.
We usually represent a Decision Tree as we did in the following diagram. The root node is at the top, and the leaves of the tree are at the bottom, the leaves represent a decision. In order to create rules from a tree, we need to start from the root node, and then we work downwards, towards the leaves. The following diagram represents a sample Decision Tree:
After studying the preceding diagram of a Decision Tree, we can obtain these rules:
If Purpose = 'Education' AND Sex = 'male' AND Age > 25 Then No Default If Purpose = 'Education' AND Sex = 'male' AND...
