Why precision and recall describe different operating problems

A vendor reports high accuracy for a routing tool. If most cases belong to one common category, that number may say little about the smaller category the buyer cares most about.

Name the positive event

Define exactly what the model is trying to identify. Scikit-learn’s evaluation documentation distinguishes precision, concerning the correctness of positive predictions, from recall, concerning the relevant cases found.

For an illustrative support classifier, the positive event might be an enquiry requiring specialist review. Incorrectly flagging a routine enquiry creates extra review; missing a specialist case creates a different consequence.

Neither error should disappear inside an unexplained average.

Connect the measure to a response

Ask who handles flagged cases, what information they need and what happens to missed cases. The acceptable trade-off depends on the workflow and requires the appropriate operational judgment.

Do not copy a threshold from another application simply because the model is similar. Review capacity, consequences and case prevalence may differ.

Evaluate relevant subgroups and input conditions where lawful and appropriate. An overall result can conceal poor performance on a specific language or document format.

Keep the underlying counts

Report the tested population and error counts alongside percentages. A small sample with few relevant cases can produce unstable-looking conclusions.

Use the results to improve the process, not only choose a model. Better intake fields or clearer categories may reduce errors more effectively than a different classifier.

Before selecting a headline metric, write one example of each error in the buyer’s own workflow. Ask the operating owner to explain its consequence. This turns abstract evaluation terminology into a decision about real work and helps prevent a vendor comparison from rewarding a score the customer cannot interpret.

GAT’s SME automation analysis asks where automation creates useful work savings. Precision and recall help explain when those savings are accompanied by a manageable review burden and when they conceal cases the system fails to recognise.