MLS-C01 Modeling Practice Question
A company is deploying a model that predicts customer churn. The model's recall for the churn class is 0.9, but precision is 0.4. The business cost of false positives is high. Which strategy would MOST likely improve precision without significantly harming recall?
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 decision threshold for the churn class
Adjusting the decision threshold to require a higher probability before predicting churn can reduce false positives (increase precision) but may lower recall. The goal is to find a threshold that balances both. Using more aggressive regularization or different algorithms may not directly control the trade-off.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Collect more data for the churn class
Why it's wrong here
More data may help but does not directly adjust precision-recall trade-off.
- ✗
Use a different algorithm such as Random Forest
Why it's wrong here
Different algorithm may change performance but not directly tune the trade-off.
- ✗
Decrease the decision threshold for the churn class
Why it's wrong here
Lower threshold increases recall but decreases precision.
- ✓
Increase the decision threshold for the churn class
Why this is correct
Higher threshold reduces false positives, improving precision, though recall may drop slightly.
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