MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is performing EDA on a dataset with 100 features. They want to reduce dimensionality by removing highly correlated features. Which TWO approaches are appropriate? (Choose TWO.)
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
✓
Compute a correlation matrix and remove one feature from each pair with correlation >0.95.
Options C and D are correct. Option C directly addresses dimensionality reduction by removing highly correlated features, which reduces redundancy. Option D uses PCA to create uncorrelated components, effectively reducing dimensionality while preserving variance. Option A is incorrect because feature importance from random forest is used for selecting features predictive of the target, not for removing correlated features per se. Option B is incorrect because VarianceThreshold removes features with low variance, not specifically for correlation. Option E is incorrect because L1 regularization (Lasso) is a modeling technique that zeroes out coefficients during model training, not a method for EDA.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use feature importance from a random forest to select top features.
Why it's wrong here
Feature importance does not specifically target correlated features.
- ✗
Remove features with low variance using VarianceThreshold.
Why it's wrong here
Low variance does not necessarily indicate high correlation with other features.
- ✓
Compute a correlation matrix and remove one feature from each pair with correlation >0.95.
Why this is correct
This directly removes redundant features.
- ✓
Use Principal Component Analysis (PCA) and select components that explain 95% of variance.
Why this is correct
PCA creates orthogonal components, reducing dimensionality.
- ✗
Apply L1 regularization (Lasso) during model training to zero out coefficients of correlated features.
Why it's wrong here
This is a modeling step, not an EDA technique.
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