The data science team at Swift Investments uses a sample of 16 loans to build a decision tree that predicts whether a loan will repay or default. Although the data science team is very familiar with decision trees and other machine learning models, they do not have much experience in the finance or lending industries. The team shows the decision tree to Royce Brown, the management accountant at Swift, who indicates that some of the decisions made by the model seem incorrect. To understand how the decision tree partitions the data, the team writes out the rules and thinks through some examples. (Click the icon to view the decision tree.) Read the requirements. Requirement 1. Identify the rules dictated by this decision tree. 1. 2. 3. 4. 5.