MLS-C01 Modeling Practice Question
A data scientist is building a regression model to predict house prices. The dataset contains many features, some of which are highly correlated. The model is overfitting. Which regularization technique should the scientist use to penalize large coefficients and perform feature selection?
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
✓
L1 regularization (Lasso)
L1 regularization (Lasso) adds a penalty equal to the absolute value of the coefficients, which can shrink some coefficients to zero, performing feature selection. L2 regularization (Ridge) penalizes squared coefficients but does not zero them out. Elastic Net combines both. Dropout is for neural networks. Option A (L2 regularization) does not perform feature selection. Option B (L1 regularization) is correct for feature selection. Option C (Elastic Net) combines both but L1 alone is simpler for feature selection. Option D (Dropout) is not applicable to linear regression.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
L2 regularization (Ridge)
Why it's wrong here
L2 regularization (Ridge) penalizes the squared coefficients and does not zero them out, so it does not perform feature selection.
- ✓
L1 regularization (Lasso)
Why this is correct
L1 regularization (Lasso) adds a penalty equal to the absolute value of the coefficients, which can shrink some coefficients to zero, performing feature selection.
- ✗
Elastic Net regularization
Why it's wrong here
Elastic Net combines L1 and L2 penalties, so it does penalise large coefficients and can shrink some to zero, but it is not the *only* technique that performs feature selection—Lasso alone achieves that with a single L1 term. The question explicitly asks for a method that both penalises large coefficients and performs feature selection, and Elastic Net is tempting because it handles multicollinearity better than Lasso, yet the stem does not require that additional robustness, making Lasso the direct match.
- ✗
Dropout
Why it's wrong here
Dropout is a technique for neural networks, not applicable to linear regression.
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