Question 743 of 1,672
MLS-C01 Modeling Practice Question
A data scientist is training a binary classifier using logistic regression. The dataset has 100 features and 1 million samples. After training, the model achieves AUC of 0.85 on the test set. The business wants to understand which features contribute most to predictions. Which technique should the data scientist 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
✓
Use the coefficients of the logistic regression model as feature importance
Coefficients of logistic regression are natural measures of feature importance.
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 t-SNE to visualize feature importance
Why it's wrong here
t-SNE is for visualization, not interpretability.
- ✓
Use the coefficients of the logistic regression model as feature importance
Why this is correct
Logistic regression coefficients indicate direction and magnitude of feature impact.
- ✗
Use a random forest model and its feature importance attribute
Why it's wrong here
The question specifies logistic regression; coefficients are directly interpretable.
- ✗
Use Principal Component Analysis (PCA) to find important components
Why it's wrong here
PCA components are linear combinations, not individual feature importance.
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Last reviewed: Jun 20, 2026
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