DA0-002 Data Analysis Practice Question
An analyst is fitting a polynomial regression model and wants to choose the degree that minimizes overfitting. Which technique should the analyst use?
⚠ Common exam trap
Many exam-takers confuse Lasso (L1) with Ridge (L2), mistakenly thinking Lasso's coefficient elimination is always better for overfitting, when in fact Ridge's smooth shrinkage is more appropriate for polynomial models where all degrees should be retained but controlled.
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
✓
Ridge regression (L2)
Ridge regression (L2) adds a penalty proportional to the square of the magnitude of coefficients, which shrinks them toward zero but does not eliminate them. This regularization reduces variance and helps prevent overfitting in polynomial regression by controlling the influence of higher-degree terms, making it the correct technique for minimizing overfitting while retaining all 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.
- ✗
Lasso regression (L1)
Why it's wrong here
Lasso can reduce overfitting by shrinking some coefficients to zero, but it may eliminate useful features too aggressively for polynomial terms.
- ✗
Principal component analysis (PCA)
Why it's wrong here
PCA reduces dimensionality but does not control for overfitting due to large coefficients; it may discard important polynomial terms.
- ✗
Stepwise selection
Why it's wrong here
Stepwise selection selects features based on statistical criteria but does not explicitly address coefficient magnitude or overfitting.
- ✓
Ridge regression (L2)
Why this is correct
Ridge regression penalizes large coefficients, which is effective for reducing overfitting in polynomial models without removing features.
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