Question 130 of 1,672
MLS-C01 Modeling Practice Question
A machine learning engineer is deploying a model that predicts customer churn. The model outputs probabilities between 0 and 1. The business requires that at least 90% of customers flagged for churn actually churn (precision >= 0.9). Currently, the model's precision is 0.85 at the default threshold of 0.5. Which threshold adjustment should the engineer consider?
⚠ Common exam trap
The trap here is that candidates often associate higher thresholds with lower recall and assume precision will drop, but in reality, increasing the threshold filters out low-confidence positives, which reduces false positives and increases precision.
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
✓
Increase the threshold to 0.7
Increasing the threshold to 0.7 raises the probability cutoff for classifying a customer as churning. This means only customers with a high predicted probability (strong model confidence) are flagged, which reduces false positives and increases precision. Since the current precision at 0.5 is 0.85 and the goal is ≥0.9, moving the threshold higher is the correct direction to achieve the required precision.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Decrease the threshold to 0.4
Why it's wrong here
Lower threshold typically decreases precision.
- ✗
Decrease the threshold to 0.3
Why it's wrong here
Decreasing threshold increases sensitivity but reduces precision.
- ✓
Increase the threshold to 0.7
Why this is correct
Higher threshold increases precision by requiring higher confidence for positive predictions.
- ✗
Keep the threshold at 0.5
Why it's wrong here
Current precision is below 0.9, so no change will not meet requirement.
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Last reviewed: Jun 24, 2026
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