MLS-C01 Modeling Practice Question
A data scientist wants to build a binary classifier to predict customer churn. The dataset has 10,000 records with 500 churners (5%). Which technique should the data scientist use to address class imbalance?
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 (Synthetic Minority Over-sampling Technique) to create synthetic samples.
SMOTE (Synthetic Minority Over-sampling Technique) creates synthetic samples for the minority class, effectively balancing the dataset without losing information. Option A (Randomly undersampling the majority class) discards potentially useful data. Option C (Assigning higher class weights to the minority class) is a valid approach but not a data-level technique; it adjusts the loss function. Option D (Downsampling the majority class) also loses data and is similar to undersampling.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Randomly undersample the majority class.
Why it's wrong here
Randomly undersampling the majority class reduces dataset size and may discard valuable information.
- ✓
Use SMOTE (Synthetic Minority Over-sampling Technique) to create synthetic samples.
Why this is correct
SMOTE generates synthetic samples for the minority class, addressing imbalance without losing data.
- ✗
Assign higher class weights to the minority class.
Why it's wrong here
Assigning higher class weights to the minority class adjusts the model's focus, but this is a cost-sensitive learning approach, not a resampling technique.
- ✗
Downsample the majority class to match the minority class size.
Why it's wrong here
Downsampling the majority class to match the minority class size loses data and may lead to underfitting.
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