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4341Confusion-Matrix Metrics 1At a fixed threshold, prevalence is 20%, TPR is 80%, and FPR is 10%. What precision does that imply?机器学习简单数值题未尝试面试订阅4342Confusion-Matrix Metrics 2A fraud model keeps TPR = 0.90 and FPR = 0.03 when deployed into a market where prevalence falls from 10% to 2%. What precision should you now expect at the same threshold?机器学习简单数值题未尝试面试订阅4343Confusion-Matrix Metrics 3Predicted probabilities are [0.8, 0.6, 0.3, 0.1] and labels are [1, 0, 1, 0]. What is the Brier score?机器学习简单数值题未尝试面试订阅4344Confusion-Matrix Metrics 4For expected calibration error with equal sample weighting, you have two nonempty bins. Bin A has probabilities [0.2, 0.3] and labels [0, 1]. Bin B has probabilities [0.8, 0.9] and labels [1, 1]. Using ECE = sum over bins of (bin fraction)*|avg confidence - accuracy|, what ECE do you get?机器学习简单数值题未尝试面试订阅4345Confusion-Matrix Metrics 5A model assigns an average predicted probability of 0.18 to a bucket containing 200 names. If the model is calibrated, how many positives should you expect in that bucket on average?机器学习简单数值题未尝试面试订阅4346Brier Score Snapshot 1At one threshold, prevalence is 5%, TPR is 80%, and FPR is 10%. What PR-space point (recall, precision) corresponds to that ROC-space operating point?机器学习中等数值题未尝试面试订阅4347Brier Score Snapshot 2Across 500 cases, a calibrated model has mean predicted probability 0.12. How many positives should you expect in total?机器学习中等数值题未尝试面试订阅4348Brier Score Snapshot 3On a validation set, the model's mean predicted probability is 9% but the observed positive rate is 6%. What calibration-in-the-large error does that imply?机器学习中等数值题未尝试面试订阅4349Brier Score Snapshot 4A thresholding rule is used on a universe where prevalence is 10%, TPR is 70%, and FPR is 5%. A false negative costs 4 units and a false positive costs 1 unit. What is the expected misclassification cost per case?机器学习中等数值题未尝试面试订阅4350Brier Score Snapshot 5A calibrated bucket contains 80 names with average predicted probability 0.35. If you actually observe 20 positives, what empirical positive rate does that bucket realize, and by how many percentage points is it under the average prediction?机器学习中等数值题未尝试面试订阅4351Asymmetric Threshold Choice 1Three candidate thresholds on the same classifier yield t=0.3 -> FP=18, FN=4; t=0.5 -> FP=9, FN=7; t=0.7 -> FP=4, FN=14. If one false negative costs 5 units and one false positive costs 1 unit(s), which threshold minimizes expected classification cost over this sample?机器学习中等数值题未尝试面试订阅4356Trade Ranking Versus Probability QualityModel A has slightly higher ROC AUC, but its probabilities are visibly overconfident; Model B has slightly lower ROC AUC but much better calibration. If you need a probability to size positions according to expected payoff, which model is usually safer?机器学习中等essay未尝试面试订阅4357Severe Imbalance Metric ChoiceYou are screening rare fraud cases with a base rate below 1%. Why is PR analysis usually more informative than ROC analysis for the operational discussion?机器学习中等essay未尝试面试订阅4358Same AUC Different ReliabilityTwo models have essentially the same ROC AUC. One is well calibrated, the other places most probabilities near 0 or 1. What should be your first concern before picking the latter?机器学习中等essay未尝试面试订阅4359Thresholds Need ContextWhy is it a mistake to ask for the 'best threshold' without specifying a cost ratio or downstream resource constraint?机器学习中等essay未尝试面试订阅4360Calibration After ShiftA model was calibrated on last year's data, but the event base rate has clearly shifted this year. What is the first calibration question you should revisit?机器学习中等essay未尝试面试订阅4361Before Comparing CurvesBefore you compare ROC and PR curves across models, what dataset property should you check first?机器学习中等essay未尝试面试订阅4362Before RecalibratingA model looks poorly calibrated. What should you check first before concluding the model itself is broken?机器学习中等essay未尝试面试订阅4363Before Picking a ThresholdWhat is the first quantity you should pin down before optimizing a classification threshold?机器学习中等essay未尝试面试订阅4364Before Trusting AUCAUC improved a little after retraining. What should you ask first before declaring the new model practically better?机器学习中等essay未尝试面试订阅