MLS-C01 Modeling Practice Question
A team is training a binary classifier and obtains a confusion matrix with 100 true positives, 10 false positives, 20 false negatives, and 200 true negatives. What is the precision of the model?
⚠ Common exam trap
Watch out — candidates often confuse precision with recall or accuracy, especially when the numbers are close, leading them to pick 0.83 (recall) or miscalculate the denominator.
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
✓
0.91
Precision is calculated as TP / (TP + FP). With 100 true positives and 10 false positives, precision = 100 / (100 + 10) = 100 / 110 ≈ 0.909, which rounds to 0.91. This metric measures how many of the positive predictions were actually correct.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
0.91
Why this is correct
Precision = 100/(100+10)=0.91.
- ✗
0.87
Why it's wrong here
That is F1: 2*(0.91*0.83)/(0.91+0.83)=0.87.
- ✗
0.94
Why it's wrong here
That is accuracy: (100+200)/(330)=0.91, not 0.94.
- ✗
0.83
Why it's wrong here
That is recall: 100/(100+20)=0.83.
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