MLS-C01 Modeling Practice Question
A data scientist is training a classification model on an imbalanced dataset where the positive class represents only 5% of the data. Which technique would BEST address the class imbalance without discarding data?
⚠ Common exam trap
The MLS-C01 exam often tests the distinction between data-level techniques (like SMOTE) and post-hoc adjustments (like threshold tuning), trapping candidates who think changing the threshold alone solves the imbalance without addressing the underlying data distribution.
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 SMOTE to generate synthetic samples for the minority class
SMOTE (Synthetic Minority Oversampling Technique) is the best choice because it generates synthetic samples for the minority class by interpolating between existing minority instances, effectively balancing the dataset without discarding any data. This avoids the information loss of undersampling and the overfitting risk of simple random oversampling, making it ideal for a 5% positive class scenario.
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 SMOTE to generate synthetic samples for the minority class
Why this is correct
SMOTE creates synthetic samples, balancing the dataset without data loss.
- ✗
Randomly undersample the majority class
Why it's wrong here
Undersampling discards data and may lose important information.
- ✗
Adjust the decision threshold to 0.95
Why it's wrong here
Threshold adjustment affects predictions but not training data distribution.
- ✗
Randomly oversample the minority class with replacement
Why it's wrong here
Oversampling with replacement duplicates existing samples, increasing risk of overfitting.
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