MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is exploring a dataset with 500 features and 100,000 observations for a regression problem. The scientist notices that many features are highly correlated with each other. Which technique should the scientist use to reduce multicollinearity and improve model interpretability during exploratory data analysis?
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
✓
Calculate Variance Inflation Factor (VIF) for each feature and remove those with VIF > 10.
Variance Inflation Factor (VIF) is a measure of multicollinearity among features. Removing features with high VIF (e.g., > 10) reduces multicollinearity and retains interpretability. Option A is incorrect because mutual information measures dependency between feature and target, not multicollinearity. Option B is incorrect because PCA creates new features that are linear combinations, reducing interpretability. Option C is incorrect because Lasso regression is a modeling technique, not typically used during exploratory data analysis, and it may not remove all correlated 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.
- ✗
Compute mutual information between each feature and the target, and keep only the top 50 features.
Why it's wrong here
Mutual information measures feature relevance, not multicollinearity among features.
- ✗
Apply Principal Component Analysis (PCA) to reduce the feature space.
Why it's wrong here
PCA reduces dimensionality but creates uninterpretable components, which may not improve interpretability.
- ✗
Use Lasso regression to select features with non-zero coefficients.
Why it's wrong here
Lasso is a modeling technique, not an exploratory analysis step.
- ✓
Calculate Variance Inflation Factor (VIF) for each feature and remove those with VIF > 10.
Why this is correct
VIF quantifies how much a feature is explained by other features; high VIF indicates multicollinearity.
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