MLS-C01 Exploratory Data Analysis Practice Question
A company is building a classification model and discovers that the target variable is imbalanced: 95% of samples belong to class A and 5% to class B. The data scientist needs to understand the distribution of numeric features for each class. Which approach is most appropriate?
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 Amazon SageMaker Data Wrangler to create histograms for each feature, grouped by class label.
The most appropriate approach for understanding the distribution of numeric features for each class is to use histograms grouped by the class label. Amazon SageMaker Data Wrangler (option C) can generate these histograms, providing a clear visual comparison of how each numeric feature is distributed across class A and class B. This is especially useful with imbalanced data (95% vs 5%) because it reveals differences in shape, central tendency, and spread without being influenced by class frequencies. Option A (t-test) tests for statistical significance but does not visualize the distribution. Option B (box plots) can show summary statistics but not the full distribution shape as effectively as histograms. Option D (correlation matrix) measures linear relationships with the target but does not show per-class feature distributions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Run a t-test for each feature to determine statistical significance between classes.
Why it's wrong here
T-tests test significance but do not visualize distributions.
- ✗
Generate box plots for each feature using Amazon QuickSight.
Why it's wrong here
Box plots show quartiles but not the full distribution shape.
- ✓
Use Amazon SageMaker Data Wrangler to create histograms for each feature, grouped by class label.
Why this is correct
Histograms grouped by class provide a clear view of feature distributions across classes.
- ✗
Compute the correlation matrix between features and the target.
Why it's wrong here
Correlation matrices show linear relationships, not distributions.
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