MLS-C01 Exploratory Data Analysis Practice Question
A data analyst is investigating a dataset where the target variable is binary (0/1). The analyst wants to check for multicollinearity among the numerical features. Which statistical measure should the analyst use?
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
✓
Variance Inflation Factor (VIF).
Variance Inflation Factor (VIF) is the correct measure to check for multicollinearity among numerical features. VIF quantifies how much a feature is correlated with other features by calculating the ratio of variance of a model with multiple features to variance of a model with one feature. Option B (Mutual information) measures dependence between features and target, not among features. Option C (Chi-square test) is for categorical variables. Option D (Pearson correlation) only measures pairwise linear relationships, not multicollinearity involving multiple 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.
- ✓
Variance Inflation Factor (VIF).
Why this is correct
VIF measures how much a feature is explained by other features.
- ✗
Mutual information between features and target.
Why it's wrong here
Mutual information does not measure multicollinearity.
- ✗
Chi-square test of independence.
Why it's wrong here
Chi-square is for categorical variables.
- ✗
Pearson correlation coefficient between each pair of features.
Why it's wrong here
Only detects pairwise collinearity, not multicollinearity.
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