{"blocks": [{"key": "24a78207", "text": "Scenario", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "2aa23f43", "text": "Platform must distinguish good users from potential bad actors using Bayesian inference while controlling error rates.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "372da447", "text": "Question", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "d4a4e57c", "text": "Given a prior probability p of a user being a bad actor and classifier outputs with known true-positive and false-positive rates, derive the posterior probability that a flagged user is truly bad. Define Type I and Type II errors in this context and explain their business impact. If 1 % of 10 million users are truly bad and the classifier has 95 % recall and 2 % false-positive rate, calculate the expected number of bad actors caught and the expected number of good users incorrectly flagged.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "150d0b5d", "text": "Hints", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "8b63bf33", "text": "Apply Bayes’ theorem, build a 2×2 confusion matrix, compute expected counts from prevalence and error rates.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}], "entityMap": {}}