MLS-C01 Modeling Practice Question
A company is building a binary classification model to predict customer churn. The dataset is highly imbalanced (95% non-churn, 5% churn). The data scientist uses SMOTE to oversample the minority class. After training a logistic regression model, the recall for the churn class is 0.80, but the precision is only 0.10. Which action 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 classification threshold for the positive class
Increasing the classification threshold for the positive class makes the model more conservative in predicting churn, which reduces false positives and improves precision. While this may slightly decrease recall (true positives), the trade-off is acceptable given the very low precision. Option A is wrong because random oversampling can lead to overfitting and does not directly address the low precision caused by threshold issues. Option B is wrong because reducing features could discard important information, potentially harming recall without necessarily improving precision. Option D is wrong because decreasing the threshold would further increase false positives, lowering precision even more.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use random oversampling instead of SMOTE
Why it's wrong here
Oversampling method may not directly improve precision; SMOTE is generally better.
- ✗
Reduce the number of features in the model
Why it's wrong here
Feature reduction may reduce model capacity and potentially harm recall.
- ✓
Increase the classification threshold for the positive class
Why this is correct
A higher threshold reduces false positives, improving precision, while likely still capturing many true positives.
- ✗
Decrease the classification threshold for the positive class
Why it's wrong here
Lowering the threshold increases false positives, worsening precision.
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