MLS-C01 Modeling Practice Question
A data scientist is performing feature selection for a linear regression model. Which TWO methods 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
✓
Lasso (L1) regularization
Both Lasso (L1) regularization and forward selection are appropriate feature selection methods for linear regression. Lasso adds an L1 penalty that shrinks some coefficients exactly to zero, effectively selecting features. Forward selection iteratively adds features based on improvement to the model. Option B (Ridge) is incorrect because L2 regularization shrinks coefficients but does not set them to zero. Option C (t-SNE) is a nonlinear dimensionality reduction technique for visualization, not feature selection. Option E (PCA) creates new components, but does not select original 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.
- ✓
Lasso (L1) regularization
Why this is correct
Lasso can zero out feature coefficients, effectively selecting features.
- ✗
Ridge (L2) regularization
Why it's wrong here
Ridge shrinks coefficients but does not set them to zero.
- ✗
t-distributed stochastic neighbor embedding (t-SNE)
Why it's wrong here
t-SNE is for dimensionality reduction for visualization, not feature selection.
- ✓
Forward selection
Why this is correct
Forward selection iteratively adds features based on performance.
- ✗
Principal component analysis (PCA)
Why it's wrong here
PCA creates new features, does not select original ones.
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