An advertising company is interested to identify if their predictive model can predict whether a person is likely to respond to a direct mailing campaign or not based on demographic attributes such as age, lifestyle, earnings and family status. They acquired a total of 200 people for validation. The results of the classification model show that out of 200 people, 70 people actually responded positive to the campaign. The classification model predicted only 55 positive responses out of 70 correctly. The classification model also predicted 120 people who did not responded (negative responses) to the campaign correctly. Use this information to fill in the blacks for the confusion matrix below: True Positives: True Negatives: I False Positives: False Negatives: Sensitivity: % Specificity: % Accuracy: % An advertising company is interested to identify if their predictive model can predict whether a person is likely to respond to a direct mailing campaign or not based on demographic attributes such as age, lifestyle, earnings and family status. They acquired a total of 200 people for validation. The results of the classification model show that out of 200 people, 70 people actually responded positive to the campaign. The classification model predicted only 55 positive responses out of 70 correctly. The classification model also predicted 120 people who did not responded (negative responses) to the campaign correctly. Use this information to fill in the blacks for the confusion matrix below: True Positives: True Negatives: I False Positives: False Negatives: Sensitivity: % Specificity: % Accuracy: %