MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is exploring a dataset with 100 features. The goal is to build a binary classification model. The dataset is highly imbalanced with 95% negative class and 5% positive class. The data scientist wants to understand the relationship between features and the target. Which technique is most appropriate for initial exploratory analysis?
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 for visualization and correlation analysis.
Stratified sampling preserves the class proportions, ensuring that the minority class (5% positive) is adequately represented in the subset for visualization and correlation analysis. Option A is wrong because removing the minority class would prevent any analysis of the target relationship. Option C is wrong because random sampling could miss the rare positive class entirely, leading to biased insights. Option D is wrong because SMOTE is a synthetic data generation technique intended for training, not for initial exploratory analysis.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Remove the minority class samples and analyze the majority class only.
Why it's wrong here
Removing the minority class defeats the purpose of understanding the target relationship.
- ✓
Use stratified sampling to create a balanced subset for visualization and correlation analysis.
Why this is correct
Stratified sampling preserves the proportion of each class and ensures the minority class is included in the analysis.
- ✗
Use random sampling to select 10% of the data for EDA.
Why it's wrong here
Random sampling may not include enough positive examples, leading to misleading insights.
- ✗
Apply SMOTE to the dataset before performing EDA.
Why it's wrong here
SMOTE is used to generate synthetic samples for model training, not for EDA.
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