AI Associate AI Fundamentals Practice Question
A data scientist is building a churn prediction model. The dataset has 95% non-churn and 5% churn. Which THREE actions should the data scientist take to address the 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
✓
Undersample the non-churn class
Oversampling minority class, undersampling majority class, and using appropriate metrics (like precision/recall) are common approaches. Using accuracy is not recommended. Training on original data without adjustment will produce a biased model.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Train the model on the original dataset without changes
Why it's wrong here
Model would be biased towards majority class.
- ✗
Use accuracy as the primary metric
Why it's wrong here
Accuracy is misleading for imbalanced data.
- ✓
Undersample the non-churn class
Why this is correct
Reduces majority class size to balance.
- ✓
Use precision, recall, or F1 score instead of accuracy
Why this is correct
These metrics are more informative for imbalanced data.
- ✓
Oversample the churn class
Why this is correct
Balances the dataset by duplicating minority examples.
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