A national bank has developed a predictive model for identifying customers who are more likely to...
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A national bank has developed a predictive model for identifying customers who are more likely to accept a credit card offer, if a customer is predicted to accept the credit card offer, he or she is classified into Classt, 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. Predicted Class 1 Probability 0.22 0.59 Customer 2 A 100 Actual Class 0.50 a1. Specify the predicted class membership for the validation data set using the cutoff value of 0.25. Produce a confusion matrix, Actual Class Class 1 Class 0 Actual Class Class 1 Class 0 Predicted Class 1 Predicted Class 0 a2. Specify the predicted class membership for the validation data set using the cutoff value of 0.50. Produce a confusion matric Predicted Class 1 Predicted Class 0 Actual Class Class 1 Class 0 LINE a3. Specify the predicted class membership for the validation data set using the cutoff value of 0.75. Produce a confusion matro Predicted Class 1 Predicted Class 0. b1. Compute the misclassification rate, accuracy rate, sensitivity, precision, and specificity of the classification model for the cutoff value of 0.25. (Round your final answers to 2 decimal places.) Misclassification rate Accuracy rate Sensitivity Precision Specificity b2. Compute the misclassification rate, accuracy rate, sensitivity, precision, and specificity of the classification model for the cutoff value of 0.50. (Round your final answers to 2 decimal places.) Misclassification rate Accuracy rate Sensitivity Precision Specificity b3. Compute the misclassification rate, accuracy rate, sensitivity, precision, and specificity of the classification model for the cutoff value of 0.75. (Round your final answers to 2 decimal places.) Misclassification rate Accuracy rate Sensitivity Precision Specificity es c1. Create a cumulative lift chart for the classification model At 60 cases, what is the cumulative response using the sorted predicted values? Cumulative response using the sorted predicted values c2. Create a decilewise lift chart for the classification model. What is the lift value of the first decile? Lift value of the first decile 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? (Round your final answer to 2 decimal places.) Lin (120) 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? (Round your final answer to 2 decimal places.) Lint (1.50) A national bank has developed a predictive model for identifying customers who are more likely to accept a credit card offer, if a customer is predicted to accept the credit card offer, he or she is classified into Classt, 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. Predicted Class 1 Probability 0.22 0.59 Customer 2 A 100 Actual Class 0.50 a1. Specify the predicted class membership for the validation data set using the cutoff value of 0.25. Produce a confusion matrix, Actual Class Class 1 Class 0 Actual Class Class 1 Class 0 Predicted Class 1 Predicted Class 0 a2. Specify the predicted class membership for the validation data set using the cutoff value of 0.50. Produce a confusion matric Predicted Class 1 Predicted Class 0 Actual Class Class 1 Class 0 LINE a3. Specify the predicted class membership for the validation data set using the cutoff value of 0.75. Produce a confusion matro Predicted Class 1 Predicted Class 0. b1. Compute the misclassification rate, accuracy rate, sensitivity, precision, and specificity of the classification model for the cutoff value of 0.25. (Round your final answers to 2 decimal places.) Misclassification rate Accuracy rate Sensitivity Precision Specificity b2. Compute the misclassification rate, accuracy rate, sensitivity, precision, and specificity of the classification model for the cutoff value of 0.50. (Round your final answers to 2 decimal places.) Misclassification rate Accuracy rate Sensitivity Precision Specificity b3. Compute the misclassification rate, accuracy rate, sensitivity, precision, and specificity of the classification model for the cutoff value of 0.75. (Round your final answers to 2 decimal places.) Misclassification rate Accuracy rate Sensitivity Precision Specificity es c1. Create a cumulative lift chart for the classification model At 60 cases, what is the cumulative response using the sorted predicted values? Cumulative response using the sorted predicted values c2. Create a decilewise lift chart for the classification model. What is the lift value of the first decile? Lift value of the first decile 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? (Round your final answer to 2 decimal places.) Lin (120) 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? (Round your final answer to 2 decimal places.) Lint (1.50)
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A 1 Validation data set Customer Actual Class Predicted class 1 probability 1 1 022 2 1 100 Cutoff v... View the full answer
Related Book For
Statistics for the Behavioral Sciences
ISBN: 9781111830991
9th edition
Authors: Frederick J Gravetter, Larry B. Wallnau
Posted Date:
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