Question 1,166 of 1,672
MLS-C01 Modeling Practice Question
A data scientist is training a binary classifier using imbalanced data. Which TWO techniques can help improve model performance on the minority class? (Choose two.)
⚠ Common exam trap
The MLS-C01 exam often tests the misconception that random undersampling is always beneficial for imbalanced data, but candidates must recognize that it can discard useful majority class patterns and that SMOTE or other synthetic oversampling methods are preferred.
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
✓
Use the F1 score as the evaluation metric.
The F1 score is the harmonic mean of precision and recall, making it a robust evaluation metric for imbalanced datasets because it captures both false positives and false negatives. Unlike accuracy, which can be misleadingly high when the majority class dominates, the F1 score provides a balanced measure of model performance on the minority class.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Undersample the majority class randomly.
Why it's wrong here
Random undersampling may discard useful data.
- ✗
Use accuracy as the evaluation metric.
Why it's wrong here
Accuracy is misleading for imbalanced data.
- ✓
Use the F1 score as the evaluation metric.
Why this is correct
F1 score balances precision and recall.
- ✓
Oversample the minority class using SMOTE.
Why this is correct
SMOTE generates synthetic samples for the minority class.
- ✗
Apply L1 regularization to the model.
Why it's wrong here
Regularization does not address class imbalance.
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Last reviewed: Jun 24, 2026
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