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Given the data from a binary classification evaluation, a subject can be compared by the subject itself or something else which will be represented by ground truth (1 means same subject 0 means different)

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Given the data from a binary classification evaluation, a subject can be compared by the subject itself or something else which will be represented by ground truth (1 means same subject 0 means different). The higher the score, the more similar the subjects are. Answer question below: 1. Draw ROC curve for FNR & FPR. Mark the point at threshold -0.4, -0.43 , -0.45. 2. Find the enroll subject ids with highest FPR & FNR at threshold -0.43 3. Based on your analysis, which threshold will you choose (-0.4, -0.43, -0.45), and why? FPR: False Positive Rate FNR: False Negative Rate