MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is performing EDA on a dataset with 100 features. They want to identify which features are most predictive of the target using a model-agnostic method. Which technique should they use?
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
✓
Permutation feature importance
Permutation feature importance is the correct model-agnostic method because it measures the increase in prediction error after permuting a feature's values, breaking the relationship with the target, and works with any model. Pearson correlation (A) is bivariate and only captures linear relationships. L1 regularization (B) is model-specific to linear models and embeds feature selection within the model. SHAP values (C) are model-specific as they rely on game theory and require model outputs for calculation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Pearson correlation matrix
Why it's wrong here
Correlation only captures linear relationships.
- ✗
L1 regularization
Why it's wrong here
L1 is a model-specific technique (e.g., Lasso).
- ✗
SHAP values
Why it's wrong here
SHAP is model-specific (requires a trained model).
- ✓
Permutation feature importance
Why this is correct
Permutation importance works with any model and measures drop in performance when a feature is shuffled.
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