MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is performing EDA on a dataset with many features. They suspect some features are redundant due to high pairwise correlations. Which technique can help identify groups of correlated features?
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
✓
Create a correlation matrix and visualize with a heatmap
Create a correlation matrix and visualize with a heatmap. This technique directly shows pairwise correlations between features, making it easy to identify groups of highly correlated (redundant) features. Option A is incorrect: t-SNE is a dimensionality reduction technique for visualization of high-dimensional data, but it does not quantify pairwise correlations between features. Option B is incorrect: PCA reduces dimensionality by creating principal components that are linear combinations of original features; while loadings indicate feature contributions, they do not directly show pairwise correlations between original features. Option C is incorrect: Mutual information measures dependency between features and target, not between features themselves. Option D is incorrect: The chi-square test is used for testing association between categorical variables, not for continuous features or pairwise correlation 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.
- ✗
Use t-SNE to visualize feature relationships
Why it's wrong here
t-SNE is for visualizing high-dimensional data in low dimensions, not for correlation analysis.
- ✗
Apply PCA and examine the loadings
Why it's wrong here
PCA loadings indicate contribution to components, not direct pairwise correlations.
- ✗
Compute mutual information between each feature and the target
Why it's wrong here
Mutual information measures dependency with target, not between features.
- ✗
Use chi-square test for each pair
Why it's wrong here
Chi-square test is for categorical features, not numerical correlations.
- ✓
Create a correlation matrix and visualize with a heatmap
Why this is correct
A correlation matrix heatmap clearly shows correlated feature groups.
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