Question 695 of 1,672
MLS-C01 Modeling Practice Question
A machine learning engineer is evaluating a binary classification model. The model has a high recall but low precision. Which of the following is the most likely consequence?
⚠ Common exam trap
The MLS-C01 exam often tests the precision-recall trade-off by asking candidates to confuse the definitions of false positives and false negatives, leading them to incorrectly associate high recall with many false positives instead of few false negatives.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The model has many false positives.
High recall means the model correctly identifies most positive cases (few false negatives), but low precision indicates that among the cases predicted as positive, many are actually negative. This directly implies a high number of false positives, as precision = TP/(TP+FP) and a low precision with high recall forces FP to be large relative to TP.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The model has many false positives.
Why this is correct
Low precision means a high rate of false positives.
- ✗
The model has few false negatives.
Why it's wrong here
High recall implies few false negatives, but that is not a consequence of low precision.
- ✗
The model misses many positive cases.
Why it's wrong here
High recall means few misses.
- ✗
The model has few false positives.
Why it's wrong here
Low precision indicates many false positives.
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Last reviewed: Jun 24, 2026
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