AI0-001 Machine Learning and Deep Learning Practice Question
A data scientist is training a binary classification model to detect fraudulent transactions. The dataset is highly imbalanced with only 1% fraud cases. Which technique is most appropriate to address the class imbalance?
⚠ Common exam trap
A common misconception is that undersampling is always better because it reduces dataset size and training time, but the trap here is that undersampling discards majority-class data, which can severely degrade model performance when the imbalance is extreme (e.g., 1:99 ratio).
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
✓
Oversample the minority class
Oversampling the minority class (e.g., using SMOTE or random oversampling) is the most appropriate technique because it balances the dataset by generating synthetic or duplicate examples of the fraud cases, allowing the model to learn the decision boundary for the minority class without discarding valuable majority-class data. This directly addresses the class imbalance where only 1% of transactions are fraudulent, improving recall and precision for fraud detection.
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 a linear regression model
Why it's wrong here
Linear regression predicts continuous values and minimises squared error, so it cannot output class probabilities for a binary fraud label; its coefficients also fit the majority class. It is tempting as a familiar baseline, and would be correct for predicting a continuous target such as transaction amount.
- ✓
Oversample the minority class
Why this is correct
Oversampling the minority class replicates or synthesises fraudulent examples so the training set becomes more balanced, letting the model learn fraud patterns rather than predicting the majority class. This satisfies the stem's 1% fraud constraint, unlike accuracy-based metrics that mislead on imbalanced data.
- ✗
Undersample the majority class
Why it's wrong here
Undersampling the majority class discards legitimate non-fraud transactions, shrinking the training set and losing information the model needs to distinguish subtle fraud patterns. It is tempting because it balances class counts cheaply, and would suit very large datasets where the majority class is genuinely redundant.
- ✗
Increase the learning rate
Why it's wrong here
Learning rate controls the size of gradient descent steps during optimisation; it does not alter the class distribution or loss weighting, so the 1% fraud minority remains dominated. It is tempting because tuning it affects convergence, but it would be the right lever for unstable or slow training, not imbalance.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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