MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is analyzing a dataset with a target variable that is heavily imbalanced (e.g., 99% negative class, 1% positive class). Which exploratory data analysis technique is most appropriate to understand the relationship between features and the target before modeling?
⚠ Common exam trap
It's easy for candidates to think random sampling (Option A) is sufficient for EDA, but they overlook that severe class imbalance (99:1) makes random samples uninformative for the minority class, whereas stratified sampling explicitly addresses this by ensuring both classes are represented in the analysis subset.
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
✓
Use stratified sampling to create a balanced subset, then compute correlation matrices and box plots.
Stratified sampling preserves the class distribution in the sample, allowing you to create a balanced subset for exploratory analysis. Computing correlation matrices and box plots on this balanced subset reveals feature-target relationships without being overwhelmed by the majority class, which is critical for imbalanced datasets like 99% negative vs. 1% positive.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Randomly sample 10% of the data and plot feature distributions by class.
Why it's wrong here
Random sampling may miss the minority class entirely.
- ✗
Apply PCA to reduce dimensionality, then visualize the first two components.
Why it's wrong here
PCA does not address class imbalance.
- ✓
Use stratified sampling to create a balanced subset, then compute correlation matrices and box plots.
Why this is correct
Stratified sampling preserves class proportions, enabling meaningful EDA.
- ✗
Focus only on the majority class features to avoid bias.
Why it's wrong here
Ignoring minority class is not appropriate.
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