MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is exploring a dataset with 50 features. Which TWO EDA techniques are most effective for detecting multicollinearity?
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
✓
Scatter plots of each feature pair
Options C and E are the most effective EDA techniques for detecting multicollinearity. Scatter plots of each pair of features (C) allow visual inspection of linear relationships between features. A correlation matrix displayed as a heatmap (E) provides a quantitative measure of pairwise correlations, making it easy to spot high correlations indicative of multicollinearity. Option A (box plots) shows univariate distributions and does not reveal relationships between features. Option B (VIF analysis) is a formal statistical test for multicollinearity, but it is not typically considered an EDA technique; EDA focuses on visual exploration. Option D (histograms) similarly only show univariate 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.
- ✗
Box plots of each feature
Why it's wrong here
Box plots show univariate distributions and do not reveal relationships between features, so they are not effective for detecting multicollinearity.
- ✗
Variance Inflation Factor (VIF) analysis
Why it's wrong here
VIF analysis is a formal statistical method for detecting multicollinearity, but it is not typically considered an EDA technique; EDA focuses on visual exploration.
- ✓
Scatter plots of each feature pair
Why this is correct
Scatter plots of each pair of features allow visual inspection of linear relationships, making them effective for detecting multicollinearity.
- ✗
Histograms of each feature
Why it's wrong here
Histograms show univariate distributions and do not reveal relationships between features, so they are not effective for detecting multicollinearity.
- ✓
Correlation matrix visualized as heatmap
Why this is correct
A correlation matrix visualized as a heatmap provides a quantitative measure of pairwise correlations, making it easy to spot high correlations indicative of multicollinearity.
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