Problem
Scenario
You are reviewing a classifier on a dataset where the positive class is rare, and the team is worried that standard evaluation can give a misleading picture of performance. You need to explain how you would judge whether the model is actually useful when most examples belong to the negative class.
Question
How do you evaluate a model on imbalanced datasets?
Observed Metrics
Recall·0.68AUC-ROC·0.93Accuracy·99.1%F1 score·0.51Precision·0.41Class prevalence·0.8%
Practicing as: AI/ML Analyst interview at IntelHi, I'll play your Intel interviewer for the AI/ML Analyst role. Candidates describe these interviews as mostly positive and moderately difficult, so expect me to be friendly and conversational. Take your time with the question above and answer like we're in the room.
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