MLS-C01 Exploratory Data Analysis Practice Question
A machine learning team is analyzing a dataset with a target variable that is highly imbalanced (99% negative class, 1% positive class). They want to understand the distribution and relationships before modeling. Which exploratory data analysis technique is most appropriate to visualize the imbalance and guide resampling strategy?
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
✓
Bar chart of class frequencies and a correlation heatmap
A bar chart of class frequencies clearly visualizes the imbalance (99% negative vs 1% positive), and a correlation heatmap helps identify which features are correlated with the target, guiding resampling strategy. Option A is wrong because a confusion matrix is used for evaluating model predictions, not for initial exploratory data analysis of class imbalance. Option B is wrong because a scatterplot matrix is designed to visualize relationships between continuous variables and can be overwhelming with many features; it does not directly highlight the class imbalance. Option C is wrong because box plots grouped by target class show feature distributions across classes but do not explicitly quantify the imbalance ratio itself.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Confusion matrix on a sample of the data
Why it's wrong here
Confusion matrix is used after model predictions, not for initial EDA.
- ✗
Scatterplot matrix of all features colored by class
Why it's wrong here
Scatterplot matrix is useful for continuous variables but does not directly show class imbalance.
- ✗
Box plots of each feature grouped by the target class
Why it's wrong here
Box plots show distribution differences but not the overall imbalance ratio.
- ✓
Bar chart of class frequencies and a correlation heatmap
Why this is correct
Bar chart shows imbalance clearly; correlation heatmap helps identify features related to the target.
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