hardMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A data scientist observes that a linear…
A data scientist observes that a linear regression model has many irrelevant features. They want to perform feature selection to improve generalization. Which method combines feature selection with model training using a penalty that can shrink coefficients to zero?
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 regression
Lasso regression uses L1 regularization to shrink coefficients to zero, effectively performing feature selection.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Ridge regression
Why it's wrong here
Ridge uses L2 regularization that shrinks coefficients but does not set them to zero.
- ✓
Lasso regression
Why this is correct
Lasso's L1 penalty forces some coefficients to exactly zero, enabling feature selection.
- ✗
Recursive Feature Elimination (RFE)
Why it's wrong here
RFE is a wrapper method that does not embed feature selection within model training.
- ✗
Principal Component Analysis (PCA)
Why it's wrong here
PCA is dimensionality reduction that creates new features, not selection of original features.
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