MLS-C01 Exploratory Data Analysis Practice Question
A machine learning team is analyzing a dataset with numerical features. They compute the pairwise correlation matrix and find that two features, 'X1' and 'X2', have a correlation coefficient of 0.98. The team plans to train a linear regression model. Which of the following actions should the team take to avoid multicollinearity issues?
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 highly correlated features.
Removing one of the highly correlated features reduces multicollinearity. Option A is wrong because PCA creates new uncorrelated features but is not necessary for just two correlated features. Option B is wrong because adding an interaction term between X1 and X2 would actually increase multicollinearity. Option C is wrong because standard scaling does not address correlation between features.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Perform PCA on the dataset to reduce dimensionality.
Why it's wrong here
Performing PCA reduces dimensionality by projecting features onto orthogonal principal components, but it does not resolve multicollinearity between X1 and X2 because the original collinear features remain in the dataset; the team would still need to remove or regularise one of them before training a linear regression model. This option is tempting because PCA is commonly used to decorrelate features and can eliminate redundancy in high-dimensional datasets, making it a correct choice when the goal is to reduce feature count while preserving variance, rather than to directly address pairwise collinearity in a small set of predictors.
- ✗
Add an interaction term between X1 and X2 to the model.
Why it's wrong here
Interaction terms can increase multicollinearity, not reduce it.
- ✗
Standardize both features using Z-score normalization.
Why it's wrong here
Standardization does not change correlation.
- ✓
Remove one of the two highly correlated features.
Why this is correct
This directly addresses multicollinearity by eliminating redundancy.
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