MLS-C01 Modeling Practice Question
A financial services company is developing a fraud detection model using a highly imbalanced dataset where fraudulent transactions are only 0.1% of the data. The data scientist has trained a gradient boosting model that achieves 99.9% accuracy but only detects 20% of actual fraud cases. The business requirement is to detect at least 80% of fraud while minimizing false positives. The data scientist has access to SageMaker and can use any built-in algorithm or custom script. Which approach should the data scientist take to meet the business requirement?
⚠ Common exam trap
Test-takers frequently assume a supervised model with threshold tuning (Option A) can solve the imbalance, but they overlook that the model's learned decision boundary is fundamentally biased, and unsupervised anomaly detection like RCF is specifically designed for such extreme imbalance scenarios.
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 Amazon SageMaker Random Cut Forest (RCF) algorithm for anomaly detection.
Amazon SageMaker Random Cut Forest (RCF) is an unsupervised anomaly detection algorithm that is well-suited for highly imbalanced datasets like this one (0.1% fraud). Unlike supervised methods that struggle with extreme class imbalance, RCF isolates anomalies by measuring how many random cuts are needed to separate a point from the rest of the data, making it effective at detecting rare fraud cases without requiring balanced training data. This approach can meet the 80% fraud detection requirement while minimizing false positives by tuning the anomaly score threshold.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Keep the model but adjust the classification threshold to increase recall.
Why it's wrong here
Adjusting threshold may increase recall but at the cost of many false positives, which is not acceptable.
- ✗
Use random under-sampling of the majority class to balance the dataset and retrain the model.
Why it's wrong here
Under-sampling discards valuable data and can hurt model performance.
- ✓
Use Amazon SageMaker Random Cut Forest (RCF) algorithm for anomaly detection.
Why this is correct
RCF is designed for anomaly detection on highly imbalanced data and can detect fraud effectively.
- ✗
Use random oversampling of the minority class to balance the dataset and retrain the model.
Why it's wrong here
Oversampling can cause overfitting and may not improve recall to 80%.
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