MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is exploring a dataset with 50 features and a binary target. The data scientist computes the correlation matrix and finds that two features, X1 and X2, have a correlation coefficient of 0.95. Which TWO actions should the data scientist consider? (Choose 2.)
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 highly correlated features from the dataset.
Removing one of the highly correlated features reduces multicollinearity, which can stabilize model coefficients and improve interpretability. Option C is correct: Principal Component Analysis (PCA) transforms the correlated features into a set of uncorrelated components, effectively addressing multicollinearity. Option A is incorrect: Log transformation is used to handle skewness or scale differences, not correlation between features. Option D is incorrect: Creating an interaction term would add a new feature that is highly correlated with the original ones, potentially increasing multicollinearity. Option E is incorrect: Imputing missing values is unrelated to feature correlation; missing value imputation addresses data completeness, not multicollinearity.
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 a log transformation to X1 and X2.
Why it's wrong here
Log transformation does not reduce correlation between variables.
- ✓
Remove one of the highly correlated features from the dataset.
Why this is correct
Removing one feature reduces multicollinearity.
- ✓
Apply Principal Component Analysis (PCA) to the feature set.
Why this is correct
PCA creates orthogonal components that are uncorrelated.
- ✗
Create an interaction term between X1 and X2.
Why it's wrong here
Interaction terms can increase multicollinearity.
- ✗
Impute missing values for X1 and X2.
Why it's wrong here
Correlation does not indicate missing values.
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