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Precision asks what fraction of positive predictions were correct. Recall asks what fraction of actual positive cases were found. F1 combines precision and recall, but it does not encode every business cost or operational constraint.
Consider a fictional spam filter. Blocking an important customer email and allowing an unwanted advertisement are both errors, but they may have very different consequences.
Before interpreting a score, check for data leakage. When comparing complete AI products, combine the error counts with a task-specific tool scorecard.
Begin with the confusion matrix
Count true positives, false positives, true negatives and false negatives. State which class you treat as positive. Report sample size and class balance so readers can interpret the numbers.
Then ask who experiences each error and what happens next. An automatic blocking system may need a different threshold from a review queue where a person examines each flagged message.
Compare thresholds on a validation set
Inspect the trade-off between precision and recall as the decision threshold changes. Keep final evaluation separate from the data used to choose that threshold. If labels are uncertain, explain how they were obtained.
Accuracy alone can conceal poor performance on a rare class. F1 alone can also hide important details. A useful report includes the error counts and the operational decision behind the selected metric.
Discussion: Which error is more expensive in your application, and does your headline metric reflect that?
Reference: Google classification metrics.
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