MLS-C01 L1 Regularization (Lasso) Practice Question
A data scientist is analyzing a dataset with 1,000 features. They suspect many features are redundant and want to reduce dimensionality before training a model. Which technique is most appropriate for identifying the most important features?
⚠ Common exam trap
Candidates often confuse dimensionality reduction with feature selection. PCA reduces dimensions by creating new features, whereas Lasso selects original features.
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 L1 regularization (Lasso) to shrink coefficients to zero
L1 regularization (Lasso) is the most appropriate technique for identifying the most important features because it performs feature selection by shrinking the coefficients of less important features to zero, effectively selecting a subset of original features. This directly identifies which features are most relevant. PCA, while a dimensionality reduction technique, creates new components that are linear combinations of original features and does not identify the importance of original features. Random forest feature importance and correlation matrix methods can identify redundant features but are less direct for selecting the most important subset.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Apply principal component analysis (PCA) and select the top components
Why it's wrong here
Principal component analysis (PCA) is a dimensionality reduction technique that transforms features into new uncorrelated components. It does not identify the importance of original features; rather, it creates new features that capture variance.
- ✓
Use L1 regularization (Lasso) to shrink coefficients to zero
Why this is correct
L1 regularization (Lasso) is correct because it shrinks coefficients of less important features to zero, thereby selecting the most important original features.
- ✗
Train a random forest and remove features with low importance
Why it's wrong here
Random forest feature importance can rank features but is typically used after training a model, not as a primary dimensionality reduction or feature selection method for high-dimensional data.
- ✗
Compute the correlation matrix and remove features with high correlation
Why it's wrong here
Computing the correlation matrix helps identify redundant features, but it does not automatically select the most important features; it requires manual removal and does not account for interactions.
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