MLS-C01 Modeling Practice Question
A company uses Amazon SageMaker to train a model for fraud detection. The dataset has 1 million samples with 200 features. The data is highly imbalanced (0.1% fraud). The team wants to use a random forest model. Which technique should they use to handle the class imbalance during training?
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
✓
Synthetic Minority Over-sampling Technique (SMOTE)
SMOTE generates synthetic samples of the minority class, effectively balancing the dataset before training. This is particularly useful for random forest as it learns from the augmented data directly. Option B (class weights) adjusts the loss function but may not work well with random forest's tree-based structure, and it's not a standard technique for this algorithm. Option C (undersampling) discards majority class data, potentially losing valuable information. Option D (threshold adjustment) is a post-training step and does not address imbalance during the training phase.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Synthetic Minority Over-sampling Technique (SMOTE)
Why this is correct
SMOTE generates synthetic samples, effectively balancing the dataset.
- ✗
Use class weights inversely proportional to class frequencies
Why it's wrong here
Class weights can be used, but SMOTE is more effective for random forest with high imbalance.
- ✗
Random undersampling of the majority class
Why it's wrong here
Undersampling can discard useful data from the majority class.
- ✗
Adjust the decision threshold after training
Why it's wrong here
Threshold tuning is done after model training, not during.
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Written by Johnson Ajibi, MSc IT Security
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