MLS-C01 Practice Question: Machine Learning Implementation and Operations
A machine learning team is building a fraud detection system using Amazon SageMaker. The training data is highly imbalanced (99% legitimate, 1% fraudulent). They need to maximize the recall of the fraud class while keeping precision above 90%. Which approach should they take?
⚠ Common exam trap
Test-takers frequently assume resampling (undersampling, oversampling, or SMOTE) or class-weight adjustments are the only ways to handle imbalance, but they overlook the simpler and more precise method of threshold tuning, which directly controls the recall-precision trade-off without altering the training data.
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
✓
Train a model using the original data, then adjust the decision threshold on the validation set to maximize recall while precision > 90%
Adjusting the decision threshold on the validation set directly optimizes the trade-off between recall and precision. By lowering the threshold, the model classifies more instances as fraud, increasing recall, while the precision constraint (≥90%) ensures the threshold is set at a point where false positives remain acceptably low. This approach works with any probabilistic classifier and does not alter the training data distribution.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Undersample the majority class to create a balanced dataset and train a Random Forest
Why it's wrong here
Undersampling loses data and may not achieve required precision.
- ✓
Train a model using the original data, then adjust the decision threshold on the validation set to maximize recall while precision > 90%
Why this is correct
Threshold tuning directly optimizes recall with a precision constraint.
- ✗
Train an XGBoost model with scale_pos_weight parameter set to 99
Why it's wrong here
Weighted training helps but does not directly control precision; threshold tuning is still needed.
- ✗
Use SMOTE to oversample the fraud class and then train a logistic regression
Why it's wrong here
SMOTE can improve recall but may reduce precision; threshold tuning is needed.
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