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Observatio bctual Clasredicted Class 1 Probability 1 0 0.26 2 0 0.62 3 0 0.22 4 0 0.38 5 1 0.35 6 0 0.06 7 1 0.77 8 1 0.31 9 1 0.4 10 0 0.31 11 1 0.84 12 0 0.82 13 0 0.49 14 0 0.46 15 0 0.06 16 0 0.03 17 1 0.57 18 0 0.24 19 1 0.9 20 0 0.41 21 0 0.36 22 0 0.46 23 0 0.47 24 0 0.32 25 0 0.55 26 0 0.46 27 1 0.55 28 0 0.17 29 0 0.51 30 1 0.91 31 0 0.19 32 1 0.91 33 0 0.1 34 0 0.35 35 0 0.38 36 1 0.13 37 0 0.35 38 0 0.49 39 0.6 40 0.13 41 0.36 42 0 0.13 43 0.39 OOOOOO 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0.46 0.94 0.05 0.09 0.16 0.51 0.08 0.26 0.22 0.37 0.55 0.09 0.4 0.07 0.47 0.51 0.24 0.53 0.09 0.6 0.42 0.41 0.55 0.15 0.44 0.22 1 0.65 0.19 0.22 0.37 0.5 0.25 0.05 0.94 0.28 0.06 0.51 0.16 0.42 0.54 0.21 0.8 0.41 85 86 87 87 88 89 90 91 92 93 94 95 96 97 98 99 100 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0.41 0.14 0.54 0.07 0.95 0.24 0.51 0.41 0.12 0.27 0.42 0.34 0.66 0.5 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 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. Actual Class Customer 1 2 Predicted Class 1 Probability 0.26 0.62 100 1 2.50 pictureClick here for the Excel Data File 0-1. Specify the predicted class membership for the validation data set using the cutoff value of 0.25. Produce a confusion matrix. Actual Class Predicted Class 1 Predicted Class o Class 1 Class o 4-2. Specify the predicted class membership for the validation data set using the cutoff value of 0.50. Produce a confusion matrix. Predicted Class 1 Predicted Class o Actual Class Class 1 Class o 2-3. Specify the predicted class membership for the validation data set using the cutoff value of 0.75. Produce a confusion matrix. Predicted Class 1 Predicted Class o Actual Class Class 1 Class o b-1. 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 b-2 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 b-3. 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 c-1. 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 c-2. Create a decile-wise 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 lit 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.) Lift (1:20) 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.) Lift (1:50) Observatio bctual Clasredicted Class 1 Probability 1 0 0.26 2 0 0.62 3 0 0.22 4 0 0.38 5 1 0.35 6 0 0.06 7 1 0.77 8 1 0.31 9 1 0.4 10 0 0.31 11 1 0.84 12 0 0.82 13 0 0.49 14 0 0.46 15 0 0.06 16 0 0.03 17 1 0.57 18 0 0.24 19 1 0.9 20 0 0.41 21 0 0.36 22 0 0.46 23 0 0.47 24 0 0.32 25 0 0.55 26 0 0.46 27 1 0.55 28 0 0.17 29 0 0.51 30 1 0.91 31 0 0.19 32 1 0.91 33 0 0.1 34 0 0.35 35 0 0.38 36 1 0.13 37 0 0.35 38 0 0.49 39 0.6 40 0.13 41 0.36 42 0 0.13 43 0.39 OOOOOO 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0.46 0.94 0.05 0.09 0.16 0.51 0.08 0.26 0.22 0.37 0.55 0.09 0.4 0.07 0.47 0.51 0.24 0.53 0.09 0.6 0.42 0.41 0.55 0.15 0.44 0.22 1 0.65 0.19 0.22 0.37 0.5 0.25 0.05 0.94 0.28 0.06 0.51 0.16 0.42 0.54 0.21 0.8 0.41 85 86 87 87 88 89 90 91 92 93 94 95 96 97 98 99 100 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0.41 0.14 0.54 0.07 0.95 0.24 0.51 0.41 0.12 0.27 0.42 0.34 0.66 0.5 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 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. Actual Class Customer 1 2 Predicted Class 1 Probability 0.26 0.62 100 1 2.50 pictureClick here for the Excel Data File 0-1. Specify the predicted class membership for the validation data set using the cutoff value of 0.25. Produce a confusion matrix. Actual Class Predicted Class 1 Predicted Class o Class 1 Class o 4-2. Specify the predicted class membership for the validation data set using the cutoff value of 0.50. Produce a confusion matrix. Predicted Class 1 Predicted Class o Actual Class Class 1 Class o 2-3. Specify the predicted class membership for the validation data set using the cutoff value of 0.75. Produce a confusion matrix. Predicted Class 1 Predicted Class o Actual Class Class 1 Class o b-1. 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 b-2 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 b-3. 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 c-1. 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 c-2. Create a decile-wise 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 lit 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.) Lift (1:20) 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.) Lift (1:50)

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