MLS-C01 Exploratory Data Analysis Practice Question
A machine learning team is building a fraud detection model. The dataset is highly imbalanced (99.9% legitimate, 0.1% fraudulent). Which EDA technique is most important to apply before modeling?
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
✓
Create a stratified train-test split to preserve the class distribution.
Stratified sampling is crucial for highly imbalanced datasets to ensure that the rare class is proportionally represented in both training and testing splits, allowing for proper evaluation. Normalization (A) is important but does not address imbalance. Removing outliers (B) could remove fraud cases. Correlation analysis (D) is useful but not the most critical step for imbalance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Normalize all numerical features to have zero mean and unit variance.
Why it's wrong here
Feature scaling is important but does not address class imbalance.
- ✗
Remove outliers from the dataset using the IQR method.
Why it's wrong here
Outliers may be fraud cases; removing them harms model performance.
- ✓
Create a stratified train-test split to preserve the class distribution.
Why this is correct
Ensures the rare class appears in both training and test sets.
- ✗
Perform correlation analysis to remove highly correlated features.
Why it's wrong here
Useful for multicollinearity, not imbalance.
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