AI0-001 AI Models and Data Engineering Practice Question
Which TWO techniques are commonly used for feature selection in machine learning? (Choose 2)
⚠ Common exam trap
CompTIA often tests the distinction between dimensionality reduction (PCA) and feature selection, where candidates mistakenly think PCA selects original features rather than creating new ones.
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) is correct because it adds a penalty equal to the absolute value of the coefficients to the loss function, which drives less important feature coefficients exactly to zero, effectively performing embedded feature selection. Recursive Feature Elimination (RFE) is correct because it is a wrapper method that repeatedly trains a model, ranks features by importance (e.g., coefficients or feature_importances_), removes the weakest feature(s), and recurses until the desired number of features remains. PCA is not a feature-selection technique but a dimensionality-reduction method that creates new uncorrelated components from the original features, so it does not select among them. SMOTE is a data-level technique for handling class imbalance by synthesizing minority-class samples, not for selecting features. Dropout is a neural-network regularization method that randomly deactivates neurons during training to reduce overfitting, and it does not perform 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.
- ✗
Principal Component Analysis (PCA)
Why it's wrong here
PCA projects features onto orthogonal principal components, producing new transformed dimensions rather than selecting a subset of the original features. It is tempting because it reduces dimensionality and is often grouped with selection methods, but it would be correct for feature extraction, where interpretability of original variables is not required.
- ✗
SMOTE
Why it's wrong here
SMOTE synthesises new minority-class samples to rebalance class distributions before training; it does not rank, score or remove input features. It is tempting because it also operates on the feature matrix, but it would be correct when addressing class imbalance, not when selecting which features to retain.
- ✓
L1 regularization (Lasso)
Why this is correct
L1 regularization adds a penalty proportional to the absolute value of coefficients, driving irrelevant feature weights exactly to zero and thereby performing embedded feature selection. This yields a sparse model, satisfying the feature-selection technique requirement rather than merely shrinking coefficients as L2 does.
- ✗
Dropout
Why it's wrong here
Dropout randomly deactivates neurons during training to reduce overfitting in neural networks; it acts on hidden units, not on input feature selection. It is tempting because it removes elements from the model, but it would be correct as a regularisation technique during training rather than a feature-selection method.
- ✓
Recursive Feature Elimination (RFE)
Why this is correct
RFE fits a model, ranks features by importance, removes the weakest, and repeats until the desired count remains. This wrapper method evaluates feature subsets against model performance, satisfying the selection requirement directly rather than merely shrinking coefficients.
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