MLS-C01 Exploratory Data Analysis Practice Question
During EDA, a data scientist discovers that two numerical features have a Pearson correlation coefficient of 0.95. Which action should the scientist take to avoid multicollinearity in 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
✓
Remove one of the features
Pearson correlation of 0.95 indicates high multicollinearity, which can adversely affect linear regression by inflating standard errors. Removing one of the correlated features (Option A) is a straightforward solution to eliminate multicollinearity. Option B (PCA) reduces dimensionality but creates principal components that are linear combinations, losing interpretability; it also does not directly remove the original features. Option C (Ridge regression) applies L2 regularization to shrink coefficients, which can mitigate multicollinearity but does not remove it; simply removing one feature is simpler. Option D (polynomial features) would introduce more correlated terms, worsening multicollinearity. Option E (min-max scaling) does not affect correlation. Therefore, removing one feature is the best action.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Remove one of the features
Why this is correct
Removing one feature eliminates multicollinearity and retains interpretability.
- ✗
Apply PCA to the two features
Why it's wrong here
PCA reduces dimensionality but the components are not interpretable as original features.
- ✗
Use Ridge regression to penalize coefficients
Why it's wrong here
Ridge regression is designed to mitigate multicollinearity by shrinking coefficients towards zero, but it does not remove features. With a correlation of 0.95, the features are highly redundant, and simply penalising their coefficients doesn't address the underlying issue of having two essentially identical predictors. Ridge regression is tempting because it is a standard technique for handling multicollinearity and is effective when you wish to retain all predictors, even if some are correlated.
- ✗
Create polynomial features from the correlated pair
Why it's wrong here
Polynomial features increase correlation and multicollinearity.
- ✗
Apply min-max scaling to both features
Why it's wrong here
Scaling does not affect correlation.
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