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Choosing a Classifier Threshold

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Problem

Scenario

You have a binary classifier that outputs probabilities, and the team needs a decision threshold for turning scores into actions. Different thresholds change precision and recall, so the right choice depends on the business cost of false positives and false negatives.

Question

How do you choose an operating threshold for a classifier?

What Matters

  • Precision and recall trade off as the threshold moves
  • Calibration matters if scores are used as probabilities
  • Class imbalance can make PR curves more useful than ROC alone
  • Operational capacity can determine the feasible threshold range