Example-Step1 Classification algorithm Training data name age income loan decision Sandy Jones youth low risky Bill Lee youth low risky Caroline Fox middle_aged high safe Rick Field middle_aged low risky Susan Lake senior low safe Classification rules Claire Phips senior medium safe Joe Smith middle_aged high safe 4年0 IF age youth THEN loan_decision risky IF income high THEN loan_decision safe IF age middle_aged AND income low THEN loan_decision risky .Learning:Training data are analyzed by a classification algorithm.Here,the class label attribute is loan decision,and the learned model or classifier is represented in the form of classification rules. ATA Copyright2019 by Xiaoyu Li.Copyright © 2019 by Xiaoyu Li. 7 Example-Step1  Learning: Training data are analyzed by a classification algorithm. Here, the class label attribute is loan decision, and the learned model or classifier is represented in the form of classification rules