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easyData Scientist interview question

Explain the trade-off between precision and recall, and how you would pick a threshold for a real model.

What the interviewer wants to hear

  • Precision is the share of flagged cases that are truly positive
  • Recall is the share of real positives that get flagged
  • Raising the threshold usually raises precision and lowers recall
  • Pick the threshold from the business cost of each error type
  • Use a precision-recall curve to see the options

Model answer

Precision asks: of everything the model flagged, how much was really positive? Recall asks: of all real positives, how many did we catch? Most models output a score, and the threshold decides what counts as a positive. If I raise the threshold, I flag fewer cases, so precision usually goes up and recall goes down. Lowering it does the opposite. There is no universally right point. I would plot the precision-recall curve and talk to the business about the cost of each mistake. For fraud review, a missed fraud might cost hundreds of dollars while a false alarm costs a few minutes of an analyst, so I would favour recall up to the team review capacity. For an email that tells customers their account is suspended, false positives are very costly, so I would favour precision. F1 is a handy summary when costs are similar.

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Take a timed easy round and get scored on every answer.

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