AI Associate Data for AI Practice Question
A telecom company uses Einstein Discovery to predict customer churn. The training dataset contains 100,000 records, but only 5% represent churned customers. The model achieves 95% accuracy on a holdout test set, but the recall for churn is only 20%. The business wants to proactively retain at-risk customers, so they need to identify as many churners as possible. What action should the data scientist take to improve churn recall?
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
✓
Oversample the minority class using SMOTE to create synthetic churn examples.
Class imbalance causes the model to favor the majority class. Oversampling the minority class (e.g., using SMOTE) balances the dataset, helping the model learn churn patterns better and improve recall.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the regularization parameter to prevent overfitting.
Why it's wrong here
Regularization addresses overfitting, not imbalance; it may not help recall if the model already underfits the minority class.
- ✗
Collect more data, especially of churned customers.
Why it's wrong here
While more data can help, it is often impractical and time-consuming; oversampling is a more immediate and effective solution.
- ✓
Oversample the minority class using SMOTE to create synthetic churn examples.
Why this is correct
SMOTE generates synthetic instances of the minority class, balancing the dataset and improving recall without losing information.
- ✗
Undersample the majority class to match the minority class size.
Why it's wrong here
Undersampling discards many non-churn records, losing valuable information and potentially reducing overall performance.
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