MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is working with a dataset that has imbalanced classes (1% positive). They want to explore the data before modeling. Which visualization technique is most appropriate to understand the distribution of features with respect to the target class?
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
✓
Parallel coordinates plot
Parallel coordinates plot can show feature patterns for minority vs majority class in high dimensions. Option A is wrong because box plots are univariate and do not show interactions between features. Option C is wrong because histograms are univariate and do not show interaction. Option D is wrong because scatter plot matrices become cluttered with many features.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Box plots grouped by class
Why it's wrong here
Box plots are univariate and do not show interactions between features; they only show distribution of a single feature per class.
- ✓
Parallel coordinates plot
Why this is correct
Parallel coordinates plot effectively displays patterns across high-dimensional data, allowing comparison of minority and majority class distributions.
- ✗
Histograms overlaid by class
Why it's wrong here
Histograms are univariate and cannot reveal multivariate patterns.
- ✗
Scatter plot matrix
Why it's wrong here
Scatter plot matrices, while multivariate, become cluttered with many features and are not ideal for visualizing class separation in high dimensions.
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