MLS-C01 Modeling Practice Question
A financial services company is building a fraud detection model using Amazon SageMaker. The dataset has 10 million transactions, with 0.1% fraudulent. They train an XGBoost model with default hyperparameters. The model achieves 99.9% accuracy on the test set, but only catches 10% of actual fraud cases. The company wants to maximize the number of fraud cases caught while keeping the false positive rate below 5%. The data scientist has already tried adjusting the class weights and threshold, but the recall is still low. What should the data scientist do next?
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 a different algorithm such as a balanced random forest or SMOTE with XGBoost
The model's high accuracy but low recall indicates it's predicting the majority class (non-fraud) most of the time due to extreme class imbalance (0.1% fraud). Default XGBoost does not handle such imbalance well. Using a balanced random forest (which under-samples the majority class during training) or SMOTE (synthetic oversampling of the minority class) with XGBoost can improve recall by making the model more sensitive to fraud. Option A (collecting more data) may not help if the imbalance ratio stays the same. Option C (PCA) reduces dimensionality but does not address class imbalance. Option D (larger instance) only affects training speed, not model performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Collect more data, especially fraudulent transactions, to balance the dataset
Why it's wrong here
Collecting more fraud data may help, but it's time-consuming and may not be feasible.
- ✓
Use a different algorithm such as a balanced random forest or SMOTE with XGBoost
Why this is correct
Balanced random forest or SMOTE are designed to handle imbalanced datasets.
- ✗
Apply PCA to reduce the number of features and prevent overfitting
Why it's wrong here
PCA does not address class imbalance.
- ✗
Use a larger instance type to train for more epochs
Why it's wrong here
More epochs won't solve underfitting of minority class.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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