A fraud-detection model flags 90 fraudulent transactions out of 100 actual fraud cases, but also flags 810 legitimate transactions as fraud. What does this result tell you about precision and recall, and which metric is more appropriate to prioritise given that the cost of missing a fraud case is very high?
- APrecision is 10% and recall is 90%. Because missing a fraud case is costly, recall should be prioritised. Correct
- BPrecision is 90% and recall is 10%. Because missing a fraud case is costly, precision should be prioritised.
- CPrecision is 10% and recall is 90%. Because so many legitimate transactions are incorrectly flagged, precision should be prioritised.
- DPrecision is 90% and recall is 90%. Accuracy is the right summary metric because both classes perform equally well.
Why A is correct: Precision is TP / (TP + FP) = 90 / (90 + 810) = 10%, meaning nine in ten flagged items are false alarms. Recall is TP / (TP + FN) = 90 / 100 = 90%, meaning nearly all real fraud is caught. When false negatives carry severe cost, maximising recall is the correct business priority.
Why B is wrong: This inverts the computed values. Precision measures the fraction of flagged cases that are truly fraud, not the fraction of real frauds caught, so the 90-out-of-100 figure is recall, not precision.
Why C is wrong: The metric values are correct here, but the business logic is inverted. When missing a fraud case is very costly, recall matters more - the harm from false negatives outweighs the inconvenience of false positives in this scenario.
Why D is wrong: Neither value is correct. Accuracy conflates true positives and true negatives into a single number and is misleading when classes are imbalanced. The scenario shows very different error rates for the two classes, making precision and recall far more informative than accuracy.