please answer both parts of the question

22-23. A credit card issuer has built two different credit scoring models that predict the propensity of customers to default on their loans. The outputs of the first model for a test dataset are shown in the table below. The outputs of the second model for the same test dataset are shown in the table below. Based on the predictions of these models, perform the following tasks to compare their performance. The ROC curve is created by plotting the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings. ROC analysis provides tools to select possibly optimal models and to discard suboptimal ones independently from (and prior to specifying) the cost context or the class distribution. ROC analysis is related in a direct and natural way to the cost/benefit analysis of diagnostic decision-making. In a typical scenario with two target levels, a prediction score in the range [0,1] is generated by a model, and a threshold of 0.5 is used to convert this score into a categorical prediction as follows: threshold(score, 0.5 ) = positive if score >=0.5, negative otherwise. Calculate the missing point (TPR, FPR) in the ROC curve for Model 1. To generate the point for Model 1, use a threshold value of 0.51 . (0.7647,0.2308) (06647,0.1308) (08947, 0.1308) (0.9412,0.0769) (0.8412,0.0669) Question 23 3 pts Calculate the missing point (TPR, FPR) in the ROC curve for Model 2. To generate the point for Model 2, use a threshold value of 0.43 . (0.6647,0.1308) (0.8412,0.0669) (05647, 0.3308) (0.7647,0.2308) (0.9412,0.0769)