Kenzi Williams is the Director of Human Resources of a high-tech company. In order to manage the high turnover rate of the IT professionals in her company, she has developed a predictive model for identifying software engineers who are more likely to leave the company within the first year. If the software engineer is predicted to leave the company within a year, he or she is classified into Class 1; otherwise, he or she is classified into Class 0. Applying the model to the validation data set generated a table that lists the actual class membership and predicted Class 1 probability of the 100 observations in the validation data set. A portion of the table is shown below.

a. Specify the predicted class membership for the validation data set using the cutoff values of 0.25, 0.5,and 0.75. Produce a confusion matrix based on the classification results from each cutoff value.

b. Compute the misclassification rate, accuracy rate, sensitivity, precision, and specificity of the classification model from each of the three cutoff values specified in part a.
c. Create a cumulative lift chart and a decile-wise lift chart for the classification model.

d. What is the lift that the classification model provides if 20% of the observations are selected by the model compared to randomly selecting 20% of the observations?

e. What is the lift that the classification model provides if 50% of the observations are selected by the model compared to randomly selecting 50% of the observations?

Transcribed Image Text:

Employee 1 2 1 100 Actual Class 0 0 I 1 Predicted Class 1 Probability 0.09 0.22 0.41