Question 455 of 1,672
MLS-C01 Modeling Practice Question
A data scientist is building a binary classifier using logistic regression. The dataset has 10 features and 100,000 observations. The model achieves 99% accuracy on the test set, but the precision is 50% and recall is 90%. Which TWO actions should the data scientist take to improve model performance? (Choose 2.)
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
✓
Adjust the decision threshold to increase precision at the cost of recall.
The model has high recall (90%) but low precision (50%), indicating many false positives. Two effective approaches are: adjusting the decision threshold (Option B) to require a higher predicted probability for the positive class, which reduces false positives and increases precision at the cost of some recall. Switching to a random forest classifier (Option C) can capture complex interactions and non-linearities, often improving precision by better separating classes. Option A: Increasing regularization strength (i.e., decreasing C in logistic regression) may help reduce overfitting but does not directly target precision; it may marginally help but is not a primary action. Option D: Collecting more data does not address the underlying class separation issue; it might even amplify imbalance. Option E: Removing features with low correlation could discard valuable information and worsen performance.
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 the regularization strength (C) in logistic regression.
Why it's wrong here
Regularization reduces overfitting but may not directly improve precision.
- ✓
Adjust the decision threshold to increase precision at the cost of recall.
Why this is correct
Lowering threshold increases recall; raising threshold increases precision.
- ✓
Use a random forest classifier instead of logistic regression.
Why this is correct
Random forest often achieves better precision-recall balance.
- ✗
Collect more training data.
Why it's wrong here
More data may help but is not a targeted fix for precision.
- ✗
Remove features that have low correlation with the target.
Why it's wrong here
Removing features may reduce performance.
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Last reviewed: Jun 20, 2026
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