MLS-C01 Modeling Practice Question
A company uses Amazon SageMaker to train a model for detecting fraudulent transactions. The dataset is highly imbalanced (99.9% legitimate, 0.1% fraudulent). Which approach is most effective to address this imbalance?
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
✓
Apply SMOTE to generate synthetic samples
SMOTE (Synthetic Minority Over-sampling Technique) generates synthetic samples for the minority class, effectively balancing the dataset without causing overfitting. Option A is less effective because while class weights can help, they do not increase the number of training examples for the minority class. Option C is wrong because random oversampling duplicates existing samples, which can lead to overfitting. Option D is not always feasible or effective as collecting more data may not be possible and does not guarantee balance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use class weights in the loss function
Why it's wrong here
Class weights are a valid approach, but SMOTE is often more effective.
- ✓
Apply SMOTE to generate synthetic samples
Why this is correct
SMOTE generates synthetic samples to balance the dataset.
- ✗
Random oversampling of the minority class
Why it's wrong here
Random oversampling can lead to overfitting.
- ✗
Collect more data for the minority class
Why it's wrong here
Collecting more data may not be feasible.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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