MLS-C01 Modeling Practice Question
A data scientist is training a linear regression model on a dataset with 10 features. After training, the model has high variance on the test set. Which technique should the data scientist use to reduce variance without significantly increasing bias?
⚠ Common exam trap
AWS often tests the distinction between L1 (Lasso) and L2 (Ridge) regularization, and the trap here is that candidates might think adding more features or using a simpler model is the only way to reduce variance, overlooking that L2 regularization can reduce variance without the drastic bias increase of feature elimination.
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 L2 regularization
L2 regularization (Ridge regression) adds a penalty term proportional to the square of the magnitude of the coefficients, which shrinks them toward zero. This reduces model complexity and variance by preventing any single feature from having an overly large influence, without eliminating features entirely, thus keeping bias relatively low.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use L2 regularization
Why this is correct
L2 regularization penalizes large coefficients, reducing variance.
- ✗
Add more features
Why it's wrong here
Adding features increases model complexity, likely increasing variance.
- ✗
Use a simpler model
Why it's wrong here
Simpler models increase bias, which may not be desired.
- ✗
Use a deeper decision tree
Why it's wrong here
Deeper trees increase variance.
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