MLS-C01 Modeling Practice Question
A data scientist is performing feature selection for a classification problem with 100 features. The data scientist wants to reduce overfitting and improve model interpretability. Which THREE methods are appropriate for feature selection? (Choose THREE.)
⚠ Common exam trap
AWS often tests the distinction between feature selection (keeping original features) and dimensionality reduction (creating new features), so candidates mistakenly choose PCA as a feature selection method when it is actually a feature extraction technique.
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
✓
Recursive Feature Elimination (RFE)
Recursive Feature Elimination (RFE) is a wrapper method that recursively removes the least important features based on a model's feature weights or coefficients, training the model multiple times to identify the optimal subset. This directly reduces overfitting by eliminating irrelevant or redundant features and improves interpretability by keeping only the most predictive 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.
- ✗
Principal Component Analysis (PCA)
Why it's wrong here
PCA reduces dimensionality by creating new components, not selecting original features.
- ✓
Recursive Feature Elimination (RFE)
Why this is correct
RFE recursively removes the least important features based on model coefficients or feature importance.
- ✓
L1 regularization (Lasso)
Why this is correct
L1 regularization shrinks some coefficients to zero, effectively selecting a subset of features.
- ✗
Adding random noise to the features
Why it's wrong here
Adding noise does not select features; it may degrade model performance.
- ✓
Feature importance from a random forest model
Why this is correct
Tree-based models provide feature importance scores that can be used to select top features.
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