MLS-C01 Modeling Practice Question
A company is building a binary classifier to predict customer churn. The dataset has 10,000 samples with 500 churners (5% positive class). After training a logistic regression model, the precision is 0.8 and recall is 0.2. Which metric should the data scientist focus on to improve the model's ability to identify churners while minimizing false positives?
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 recall
The model's recall is very low (0.2), meaning it misses most churners. Since the goal is to identify churners (positive class), improving recall should be the primary focus. Option A (accuracy) is misleading due to class imbalance. Option B (precision) is already high (0.8), so further improvement would likely reduce recall. Option D (F1 score) balances precision and recall, but the immediate need is to address the low recall, not to balance both.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase accuracy
Why it's wrong here
Accuracy is misleading due to class imbalance; a model that predicts majority class can have high accuracy.
- ✗
Increase precision
Why it's wrong here
Precision is already 0.8, which is acceptable; low recall is the main issue.
- ✓
Increase recall
Why this is correct
Recall is low (0.2), so improving it will capture more churners.
- ✗
Increase F1 score
Why it's wrong here
F1 is a harmonic mean; improving recall will also improve F1, but the direct goal is recall.
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