MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is exploring a dataset with 200 features. They compute the pairwise correlation matrix and notice that many features have correlations above 0.95. They want to reduce redundancy before modeling. Which of the following techniques is most appropriate for identifying and removing highly correlated features?
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
✓
Perform hierarchical clustering on the correlation matrix and select one feature per cluster.
Hierarchical clustering on correlations groups correlated features; then one can select a representative from each cluster. Option A is wrong because mutual information with the target does not capture pairwise redundancy among features. Option B is wrong because PCA creates new features but does not remove original ones. Option C is wrong because Lasso regression performs feature selection but may not handle multicollinearity well and does not directly identify redundant groups.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Compute mutual information between each feature and the target.
Why it's wrong here
Mutual information assesses relevance to target, not redundancy among features.
- ✗
Apply PCA and keep the first 50 components.
Why it's wrong here
PCA creates new features that are combinations of original ones, losing interpretability.
- ✗
Use Lasso regression to select features.
Why it's wrong here
Lasso can select features but may arbitrarily choose among correlated ones.
- ✓
Perform hierarchical clustering on the correlation matrix and select one feature per cluster.
Why this is correct
This systematically removes redundancy while retaining representative features.
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