MLS-C01 Modeling Practice Question
A company uses Amazon SageMaker to train a model for fraud detection. The training data is highly imbalanced. The data scientist uses SMOTE to oversample the minority class. However, the model still has poor recall on the minority class. Which additional technique should the data scientist consider?
⚠ Common exam trap
Candidates often assume SMOTE alone is sufficient to fix imbalance, but the exam tests the understanding that oversampling must be paired with a cost-sensitive learning technique like class weighting to directly influence the model's optimization objective.
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 class weights in the loss function
SMOTE generates synthetic samples for the minority class, but it does not directly address the model's tendency to prioritize the majority class during training. By assigning higher class weights to the minority class in the loss function, the model penalizes misclassifications of minority samples more heavily, which directly improves recall on that class. This technique is especially effective when combined with oversampling, as it forces the optimizer to focus on the underrepresented class during gradient updates.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
One-vs-rest encoding
Why it's wrong here
One-vs-rest is for multi-class.
- ✓
Use class weights in the loss function
Why this is correct
Class weights penalize minority errors more.
- ✗
L1 regularization
Why it's wrong here
L1 may not help with imbalance.
- ✗
Principal component analysis (PCA)
Why it's wrong here
PCA is for dimensionality reduction.
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Written by Johnson Ajibi, MSc IT Security
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