MLA-C01 Data Preparation for Machine Learning Practice Question

A company is building a fraud detection model on an imbalanced dataset (99% legitimate, 1% fraudulent). To improve recall on the minority class, they want to resample data. Which combination of techniques should they use?

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

✓

SMOTE on training set only

SMOTE should be applied only to the training set to avoid data leakage; evaluation must reflect the original distribution. Random undersampling may discard useful majority samples; random oversampling before split leaks information.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
SMOTE on entire dataset before train/test split
Why it's wrong here
Applying SMOTE before split causes data leakage as synthetic samples appear in both training and test sets.
✗
Random oversampling of minority class before train/test split
Why it's wrong here
Oversampling before split duplicates minority instances across sets, leading to overoptimistic performance.
✗
Random undersampling of majority class
Why it's wrong here
Undersampling discards majority data, potentially losing useful patterns.
✓
SMOTE on training set only
Why this is correct
Correct: SMOTE generates synthetic minority samples on the training set without affecting the test distribution.

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MLA-C01 Data Preparation for Machine Learning Practice Question

Why each option matters

Option-by-option breakdown

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