MLS-C01 Modeling Practice Question
A data scientist is tuning a linear regression model and observes that the model has high bias and low variance. Which action is most likely to improve model performance?
⚠ Common exam trap
AWS often tests the bias-variance tradeoff by presenting high bias (underfitting) and high variance (overfitting) scenarios, and the trap here is that candidates mistakenly choose to increase regularization or reduce features, which are remedies for overfitting, not underfitting.
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
✓
Add more features
High bias and low variance indicate underfitting, meaning the model is too simple to capture the underlying patterns in the data. Adding more features increases model complexity, allowing it to learn more relevant relationships and reduce bias. This directly addresses the core issue of underfitting in linear regression.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reduce the number of features
Why it's wrong here
Would increase bias.
- ✗
Increase regularization
Why it's wrong here
Would increase bias.
- ✓
Add more features
Why this is correct
Increases complexity, reducing bias.
- ✗
Reduce the amount of training data
Why it's wrong here
Would worsen bias.
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