MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is exploring a dataset and finds that the correlation between two features is 0.95. What should the data scientist do to address multicollinearity before training 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 two features
Removing one of the highly correlated features reduces multicollinearity. Regularization (B) like Ridge can help but does not remove multicollinearity. Scaling (C) does not affect correlation, so it does not address multicollinearity. PCA (D) can reduce multicollinearity by creating uncorrelated components, but it changes interpretability and is not the simplest solution.
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 two features
Why this is correct
Removing one feature eliminates the high correlation.
- ✗
Apply L2 regularization
Why it's wrong here
Regularization can reduce coefficients but does not remove multicollinearity.
- ✗
Standardize the features
Why it's wrong here
Standardization does not change correlation.
- ✗
Apply Principal Component Analysis
Why it's wrong here
PCA transforms features and reduces dimensionality but loses interpretability.
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