MLS-C01 Modeling Practice Question
A data scientist is training a linear regression model and wants to handle multicollinearity among features. Which TWO actions are appropriate?
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 Ridge regression (L2 regularization)
Ridge regression (L2) adds a penalty that can reduce the impact of correlated features. Removing one of the correlated features directly addresses multicollinearity. Lasso (L1) may also help but is less effective for groups of correlated features. Scaling features does not remove collinearity. Adding interaction terms increases multicollinearity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Add interaction terms between features
Why it's wrong here
Adding interaction terms can increase multicollinearity.
- ✓
Use Ridge regression (L2 regularization)
Why this is correct
Ridge regression shrinks coefficients of correlated features, reducing their impact.
- ✗
Use Lasso regression (L1 regularization)
Why it's wrong here
Lasso tends to pick one feature from a correlated group, but is less stable.
- ✓
Remove one of the highly correlated features
Why this is correct
Eliminates the collinearity directly.
- ✗
Scale all features to have zero mean and unit variance
Why it's wrong here
Scaling does not affect collinearity.
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