10.8.2: Critical Thinking Questions
- Page ID
- 58336
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Critical Thinking Questions
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A hospital wants to predict which patients are at risk of readmission within 30 days. Should they optimize for precision or recall? What are the consequences of false positives (predicting readmission that doesn't happen) vs. false negatives (missing a patient who will be readmitted)?
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A marketing team uses K-means clustering to segment 50,000 customers into 5 groups. How do you evaluate whether the clustering is "good"? What methods help determine the right number of clusters? How do you translate cluster labels into actionable marketing strategies?
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An executive says: "Our model is 97% accurate, so we should deploy it immediately." What questions should you ask before agreeing? Consider: class imbalance, what errors cost, whether the model was tested on truly unseen data, and whether the training data is representative.


