MLS-C01 Modeling Practice Question
A data scientist is training a linear regression model and wants to check for multicollinearity among the features. Which TWO methods can be used to detect multicollinearity? (Choose TWO.)
⚠ Common exam trap
AWS often tests the distinction between diagnosing model fit (R-squared, residual plots) and diagnosing predictor multicollinearity, leading candidates to mistakenly choose methods that evaluate model performance rather than feature interdependence.
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 the correlation matrix between features
Computing the correlation matrix between features directly reveals pairwise linear relationships. High correlation coefficients (e.g., >0.8 or <-0.8) between two predictors indicate potential multicollinearity, which can destabilize coefficient estimates in linear regression.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Examine the R-squared value of the model
Why it's wrong here
R-squared measures the proportion of variance explained, not collinearity among predictors.
- ✓
Compute the correlation matrix between features
Why this is correct
High pairwise correlations between features (e.g., >0.8) suggest multicollinearity.
- ✗
Check the p-values of the coefficients
Why it's wrong here
P-values indicate whether a coefficient is statistically significant, not collinearity.
- ✓
Calculate Variance Inflation Factor (VIF) for each feature
Why this is correct
VIF quantifies how much the variance of a coefficient is inflated due to collinearity; a high VIF indicates multicollinearity.
- ✗
Plot the residuals vs. fitted values
Why it's wrong here
Residual plots are used to check for homoscedasticity and linearity, not multicollinearity.
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