AI0-001 AI Concepts and Techniques Practice Question
A data scientist needs to select a regression model to predict house prices. The dataset contains many features, some of which are irrelevant. Which TWO algorithms are BEST suited for this scenario, and why? (Select 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 regression (L1 regularization)
Random Forest handles irrelevant features well via feature importance. Lasso (L1) regression performs automatic feature selection by shrinking coefficients to zero.
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 (L2 regularization)
Why it's wrong here
Ridge shrinks coefficients but does not eliminate irrelevant features.
- ✗
Linear regression
Why it's wrong here
Linear regression is sensitive to irrelevant features and does not perform feature selection.
- ✓
Lasso regression (L1 regularization)
Why this is correct
Lasso applies L1 penalty, driving coefficients of irrelevant features to zero.
- ✗
K-Nearest Neighbors
Why it's wrong here
KNN is sensitive to irrelevant features and distance metrics; not ideal.
- ✓
Random Forest
Why this is correct
Random Forest can handle many features and provides feature importance to ignore irrelevant ones.
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Written by Johnson Ajibi, MSc IT Security
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