Question 1,024 of 1,672
MLS-C01 Modeling Practice Question
A data scientist is training a binary classification model to predict customer churn. The dataset has 10,000 samples with 500 churners (5% positive class). Which TWO techniques should the scientist use to address the class imbalance? (Choose TWO.)
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 oversample the minority class
(SMOTE) generates synthetic samples for the minority class, effectively balancing the dataset. Option E (class_weight='balanced') adjusts the loss function to penalize misclassifications of the minority class more heavily. Option B (tuning threshold after training) is a post-processing step, not a technique to address imbalance during training. Option C (random undersampling) can discard useful data, leading to loss of information. Option D (oversampling by duplication) can cause overfitting due to repeated copies of the same samples.
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 oversample the minority class
Why this is correct
SMOTE creates synthetic samples to balance classes.
- ✗
Tune the decision threshold after training
Why it's wrong here
Threshold tuning addresses the decision boundary but not training imbalance.
- ✗
Randomly undersample the majority class to match minority size
Why it's wrong here
Undersampling can lose valuable data; not always best.
- ✗
Oversample the minority class by duplicating existing samples
Why it's wrong here
Simple duplication can lead to overfitting.
- ✓
Set class_weight='balanced' in the classifier
Why this is correct
This adjusts weights inversely proportional to class frequencies.
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Last reviewed: Jun 20, 2026
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