Daniella Lara, a human resources manager at a large tech consulting firm, has been reading about using analytics to predict the success of new employees. With the fast-changing nature of the tech industry, some employees have had difficulties staying current in their field and have missed the opportunity to be promoted into a management position. Daniella is particularly interested in whether or not a new employee is likely to be promoted into a management role after 10 years with the company. She gathers information on current employees who have worked for the firm for at least 10 years. The information is based on the job application that the employees provided when they originally applied for a job at the firm. For each employee, the following variables are listed: Promoted (1 if promoted within 10 years, 0 otherwise), GPA (college GPA at graduation), Sports (number of athletic activities during college), and Leadership (number of leadership roles in student organizations). A portion of the HR_Data worksheet is shown in the accompanying table.

a. Create a bagging ensemble classification tree model to determine whether a new employee is likely to be promoted into a management role after 10 years with the company. What are the overall accuracy rate, sensitivity, and specificity of the model on the validation data? What is the AUC value of the model? 

b. Create a random forest ensemble classification tree model. Select two predictor variables randomly to construct each weak learner. What are the overall accuracy rate, sensitivity, and specificity of the model on the validation data? What is the AUC value of the model? Which is the most important predictor variable? 

c. Score the new cases in the HR_Score worksheet using the random forest ensemble classification tree model. What is the probability of the first new employee being promoted within 10 years? How many new employees in the data set will likely be promoted within 10 years based on a cutoff probability value of 0.5?

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Promoted GPA Sports Leadership 0 3.28 0 2 1 3.93 6 : E 3.54 5 ⠀ 0 WN 3 ⠀ 0