MLS-C01 Exploratory Data Analysis Practice Question
During exploratory data analysis, a data scientist observes a strong correlation (r=0.95) between two numeric features. The model to be trained is a linear regression. What is the most appropriate action?
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
✓
Remove one of the correlated features.
High correlation (r=0.95) between two features indicates severe multicollinearity in linear regression, which can cause unstable coefficient estimates and inflated standard errors. The most straightforward solution is to remove one of the correlated features (Option B), as it directly eliminates the redundancy. Option A (standardization) does not affect correlation. Option C (L2 regularization) can help but is not the first choice because removal is simpler and preserves interpretability; regularization only shrinks coefficients but does not remove the linear dependence. Option D (interaction term) would increase multicollinearity, making the problem worse.
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 standardization to both features.
Why it's wrong here
Standardization does not reduce multicollinearity.
- ✓
Remove one of the correlated features.
Why this is correct
Removing reduces multicollinearity in linear regression.
- ✗
Use L2 regularization (Ridge regression) without removing features.
Why it's wrong here
Retaining both features when they exhibit high multicollinearity prevents the model from isolating individual feature effects, leading to unstable coefficient estimates. While L2 regularization is effective for mitigating overfitting by shrinking coefficients towards zero in scenarios with many small-effect predictors, it fails to address the underlying redundancy. In this case, removing one of the highly correlated variables or using dimensionality reduction resolves the structural dependency that Ridge regression merely penalises.
- ✗
Create an interaction term between the two features.
Why it's wrong here
Interaction may worsen multicollinearity.
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