Precision and Recall Quiz

Answers and explanations appear as you take the quiz.

1. Precision answers which question?
   • Of all actual positives, how many did we catch?
   • Of all items we predicted positive, how many were truly positive?
   • How accurate is the model overall?
   • How many true negatives did we find?
2. Recall is also known as:
   • Specificity
   • Accuracy
   • Precision
   • Sensitivity (true positive rate)
3. On a fixed evaluation set, lowering a positive-class decision threshold usually produces which tradeoff?
   • Recall can increase while precision can fall
   • Precision and recall must increase by equal amounts
   • The model necessarily predicts fewer positives
   • Neither metric can ever change
4. For a disease screening test where missing a sick patient is far worse than a false alarm, you should prioritize:
   • High precision
   • Low recall
   • High recall
   • True negative rate only
5. The F1 score is defined as the:
   • Arithmetic mean of precision and recall
   • Harmonic mean of precision and recall
   • Sum of precision and recall
   • Product of accuracy and recall
6. Why can precision and recall reveal a weakness hidden by high accuracy on a dataset dominated by negatives?
   • They focus on positive predictions and actual positives instead of rewarding a large true-negative count
   • They measure the true-negative rate directly
   • They must both exceed accuracy
   • They guarantee reliability regardless of sample size
7. A classifier has 20 true positives, 5 false positives, and 10 false negatives. Which precision and recall values follow?
   • Precision 2/3; recall 4/5
   • Precision 20/35; recall 20/35
   • Precision 1/4; recall 1/2
   • Precision 4/5; recall 2/3
8. Six of the top ten search results are relevant. What is precision@10 for that result list?
   • 100%
   • 40%
   • 60%
   • 6%