MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is analyzing a dataset with high multicollinearity. Which TWO techniques can help identify and address 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
✓
Use Principal Component Analysis (PCA)
Correct options: D and E. Variance Inflation Factor (VIF) (E) is a key metric for detecting multicollinearity by measuring how much the variance of a coefficient increases due to collinearity. PCA (D) addresses multicollinearity by transforming correlated features into orthogonal components. Option A is incorrect because a correlation matrix only shows pairwise correlations and may miss higher-order multicollinearity. Option B is incorrect because Lasso regression performs feature selection by shrinking coefficients but does not directly identify multicollinearity. Option C is incorrect because Recursive Feature Elimination (RFE) is a feature selection method that does not detect multicollinearity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Plot a correlation matrix
Why it's wrong here
Incorrect: Correlation matrix only shows pairwise relationships, not multicollinearity.
- ✗
Apply Lasso regression
Why it's wrong here
Incorrect: Lasso can reduce features but does not identify multicollinearity.
- ✗
Use Recursive Feature Elimination (RFE)
Why it's wrong here
Incorrect: RFE selects features but does not address multicollinearity.
- ✓
Use Principal Component Analysis (PCA)
Why this is correct
Correct: PCA creates uncorrelated components.
- ✓
Compute Variance Inflation Factor (VIF)
Why this is correct
Correct: VIF measures how much a feature is explained by others.
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