MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is exploring a dataset with many features and suspects that some features are highly correlated. Which TWO methods can the scientist use to detect and handle multicollinearity before building a linear regression model?
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
✓
Compute Variance Inflation Factor (VIF) for each feature and remove features with VIF > 10.
Options C and E are correct. Variance Inflation Factor (VIF) is a standard metric to detect multicollinearity; removing features with VIF > 10 reduces multicollinearity. Ridge regression (L2 regularization) can also handle multicollinearity by shrinking coefficients, which stabilizes estimates even when predictors are correlated. Option A is incorrect because PCA reduces dimensionality but the resulting components are orthogonal, not the original features, and using all components does not address multicollinearity among the original predictors. Option B is incorrect because standardizing features only changes their scale, not their correlations. Option D is incorrect because stepwise selection does not directly detect or mitigate multicollinearity; it can even produce unstable models if collinearity is present.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Apply Principal Component Analysis (PCA) and use all components.
Why it's wrong here
PCA reduces interpretability and does not handle multicollinearity in original features.
- ✗
Standardize all features to have zero mean and unit variance.
Why it's wrong here
Standardization does not change correlations between features.
- ✓
Compute Variance Inflation Factor (VIF) for each feature and remove features with VIF > 10.
Why this is correct
VIF detects multicollinearity; removing high VIF features reduces it.
- ✗
Use stepwise feature selection.
Why it's wrong here
Stepwise selection does not specifically address multicollinearity.
- ✓
Use Ridge regression (L2 regularization) to shrink coefficients.
Why this is correct
Ridge regression penalizes large coefficients, mitigating multicollinearity.
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