1. Probability of Bad Account Friend Request Suppose 1% of accounts on Facebook are classified as bad accounts. These bad accounts send friend requests at a rate 10 times higher than good accounts. - If a user receives one friend request, what is the probability that it comes from a bad account? - If a user receives five friend requests, what is the probability that at least one of them is from a bad account? 2. Classification Model Performance Assume we develop a classification model to detect bad accounts. During evaluation, the model achieves both a true positive rate (TPR) of 95% and a true negative rate (TNR) of 95%. - If the model predicts an account as bad, what is the probability that the account is indeed bad? 3. Feature Engineering for Bad Account Detection What types of data would you use to determine whether an account should be classified as a bad account or a good account? 4. Assessing the Bad Account Problem How would you determine whether bad accounts pose a significant issue to the platform? Would you use stratified sampling, random sampling, or another approach? 5. Defining a Bad User How would you define a “bad user” in the context of a social media platform? 6. Impact of Fraudulent Users What are the potential impacts of fraudulent or bad users on the platform and its community? 7. Impact of Friend Requests from Bad Accounts What potential effects might arise from friend requests initiated by bad accounts? 8. Precision–Recall Tradeoff When building a machine learning model to identify bad accounts, how would you approach the tradeoff between precision and recall? In which situations would you prioritize one over the other?