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11-22 Gini impurity. Laurie Rech is a management accountant at Donnelly Bank, which has recently suffered significant loan losses. Rech and her team are worried that they do not fully understand the risk profile of their loans, so they would like some way of identifying loans that are likely to default. One team member suggests developing a decision tree so that loans can be quickly and easily classified as "likely to default" or "likely to repay." After plotting and inspecting a sample of seven loans (three loans that defaulted and four loans that repaid), Rech and her team notice there are two different cuts that result in only one misplaced observation: Credit Score = 650 and Income = $70,000. Based on this, they conclude that either would be fine to use as the first node for their decision tree. Repay Default Credit Score 750 700 650 600 550 500 55 60 65 70 75 80 85 90 Annual Income ($000s) 1. Are Rech and her team correct that the two cuts are equivalent? Which one would you choose to be the first node of the decision tree? 2. Why might Rech care about Gini impurities when making decisions?

11-22 Gini impurity. Laurie Rech is a management accountant at Donnelly Bank, which has recently suffered significant loan losses. Rech and her team are worried that they do not fully understand the risk profile of their loans, so they would like some way of identifying loans that are likely to default. One team member suggests developing a decision tree so that loans can be quickly and easily classified as "likely to default" or "likely to repay." After plotting and inspecting a sample of seven loans (three loans that defaulted and four loans that repaid), Rech and her team notice there are two different cuts that result in only one misplaced observation: Credit Score = 650 and Income = $70,000. Based on this, they conclude that either would be fine to use as the first node for their decision tree. Repay Default Credit Score 750 700 650 600 550 500 55 60 65 70 75 80 85 90 Annual Income ($000s) 1. Are Rech and her team correct that the two cuts are equivalent? Which one would you choose to be the first node of the decision tree? 2. Why might Rech care about Gini impurities when making decisions?

Managerial Economics: A Problem Solving Approach
Managerial Economics: A Problem Solving Approach
5th Edition
ISBN:9781337106665
Author:Luke M. Froeb, Brian T. McCann, Michael R. Ward, Mike Shor
Publisher:Luke M. Froeb, Brian T. McCann, Michael R. Ward, Mike Shor
Chapter17: Making Decisions With Uncertainty
Section: Chapter Questions
Problem 17.1IP
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11-22 Gini impurity. Laurie Rech is a management accountant at Donnelly Bank, which has recently suffered significant loan losses. Rech and her team are worried that they do not fully understand the risk profile of their loans, so they would like some way of identifying loans that are likely to default. One team member suggests developing a decision tree so that loans can be quickly and easily classified as "likely to default" or "likely to repay." After plotting and inspecting a sample of seven loans (three loans that defaulted and four loans that repaid), Rech and her team notice there are two different cuts that result in only one misplaced observation: Credit Score = 650 and Income = $70,000. Based on this, they conclude that either would be fine to use as the first node for their decision tree. • Repay • Default 750 700 650 600 550 500 55 80 85 Annual Income (S000s) 60 65 70 75 90 1. Are Rech and her team correct that the two cuts are equivalent? Which one would you choose to be the first node of the decision tree? 2. Why might Rech care about Gini impurities when making decisions? Credit Score
Transcribed Image Text:11-22 Gini impurity. Laurie Rech is a management accountant at Donnelly Bank, which has recently suffered significant loan losses. Rech and her team are worried that they do not fully understand the risk profile of their loans, so they would like some way of identifying loans that are likely to default. One team member suggests developing a decision tree so that loans can be quickly and easily classified as "likely to default" or "likely to repay." After plotting and inspecting a sample of seven loans (three loans that defaulted and four loans that repaid), Rech and her team notice there are two different cuts that result in only one misplaced observation: Credit Score = 650 and Income = $70,000. Based on this, they conclude that either would be fine to use as the first node for their decision tree. • Repay • Default 750 700 650 600 550 500 55 80 85 Annual Income (S000s) 60 65 70 75 90 1. Are Rech and her team correct that the two cuts are equivalent? Which one would you choose to be the first node of the decision tree? 2. Why might Rech care about Gini impurities when making decisions? Credit Score
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