MLS-C01 Modeling Practice Question
A company is building a fraud detection model using a random forest classifier. The dataset is highly imbalanced with 99% legitimate transactions and 1% fraudulent. The model currently achieves 99% accuracy on the test set, but the fraud recall is only 10%. The business requires at least 80% recall for fraud. The data scientist has tried oversampling the minority class and adjusting class weights, but recall remains below 40%. The dataset contains millions of transactions with hundreds of features. Which approach should the data scientist try next to improve fraud 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
✓
Use a gradient boosting machine (e.g., XGBoost) with scale_pos_weight parameter
The current model uses random forest but still has low recall after oversampling and class weights. Gradient boosting machines like XGBoost can handle imbalanced data well with the scale_pos_weight parameter, which adjusts the weight of the positive class. This approach often yields better recall than random forest on very imbalanced datasets. Option A (random undersampling) would discard too much majority data and reduce model performance. Option C (PCA) is not directly targeting the imbalance issue and may remove important features. Option D (logistic regression) is a linear model that typically underperforms on complex, high-dimensional data with severe imbalance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Randomly undersample the majority class to a 50:50 ratio
Why it's wrong here
Undersampling discards many legitimate samples, hurting performance.
- ✓
Use a gradient boosting machine (e.g., XGBoost) with scale_pos_weight parameter
Why this is correct
Gradient boosting often outperforms random forest on imbalance with proper weighting.
- ✗
Apply PCA to reduce dimensionality before training
Why it's wrong here
PCA may remove useful features for fraud detection.
- ✗
Use a logistic regression model with L2 regularization
Why it's wrong here
Logistic regression may not capture complex patterns.
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