Question 1,050 of 1,672
MLS-C01 Modeling Practice Question
A data scientist is training a binary classifier using a large dataset with class imbalance (90% negative, 10% positive). After training a logistic regression model, the F1 score is low but accuracy is high. 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
✓
Switch to evaluation metrics such as F1 score or AUC-ROC instead of accuracy.
(switch to evaluation metrics like F1 or AUC-ROC) is correct because accuracy is misleading for imbalanced datasets. Option D (resample using SMOTE) is correct because it balances the classes and helps the model learn the minority class better. Option B (feature scaling) is a general preprocessing step but does not directly address class imbalance. Option C (add more features) may not help if the model is already biased toward the majority class. Option E (increase regularization) might reduce overfitting but does not target the imbalance issue.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Switch to evaluation metrics such as F1 score or AUC-ROC instead of accuracy.
Why this is correct
Correct: Metrics like F1 are robust to class imbalance.
- ✗
Apply feature scaling to ensure all features contribute equally.
Why it's wrong here
Incorrect: Feature scaling is beneficial but not specific to class imbalance.
- ✗
Add more features to the model to improve its capacity.
Why it's wrong here
Incorrect: Adding features without addressing imbalance may not help.
- ✓
Resample the training data using techniques like SMOTE to balance the classes.
Why this is correct
Correct: Resampling addresses class imbalance.
- ✗
Increase the regularization parameter to reduce overfitting.
Why it's wrong here
Incorrect: Regularization doesn't directly address class imbalance.
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Last reviewed: Jun 20, 2026
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