MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is exploring a dataset with many features and wants to detect multicollinearity. Which technique should the scientist 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
✓
Calculate the Variance Inflation Factor (VIF) for each feature.
Variance Inflation Factor (VIF) is a standard metric for detecting multicollinearity. Option D (pairwise scatter plots) can hint at relationships but does not quantify multicollinearity. Option B (Pearson correlation matrix) shows pairwise linear correlation but does not capture multicollinearity among multiple variables. Option C (ANOVA) is used for comparing means, not for detecting 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.
- ✓
Calculate the Variance Inflation Factor (VIF) for each feature.
Why this is correct
Variance Inflation Factor (VIF) measures how much the variance of a regression coefficient is inflated due to multicollinearity. A high VIF indicates strong multicollinearity, making it a quantitative method for detection.
- ✗
Compute the Pearson correlation matrix between features.
Why it's wrong here
The Pearson correlation matrix only shows pairwise linear relationships. It does not detect multicollinearity involving three or more features simultaneously, so it is insufficient for this purpose.
- ✗
Perform ANOVA on each feature against the target.
Why it's wrong here
ANOVA (Analysis of Variance) compares the means of groups to determine if they are significantly different. It is not designed to detect multicollinearity among predictor variables.
- ✗
Create pairwise scatter plots of all features.
Why it's wrong here
Pairwise scatter plots provide a visual inspection of relationships between two features at a time. However, they do not quantify multicollinearity and cannot reveal high-dimensional interactions among multiple features.
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