Question 836 of 1,672
MLS-C01 Modeling Practice Question
A data scientist trains a linear regression model to predict house prices. The model has high bias (underfitting). Which action is most likely to reduce bias?
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
✓
Increase model complexity
Increasing model complexity (e.g., adding polynomial features or using a more flexible algorithm) can reduce bias. Adding L1 regularization increases bias, reducing features reduces complexity, and lowering max_depth for a tree also increases bias.
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
Removing features may increase bias if they are relevant.
- ✗
Decrease the maximum depth of the tree
Why it's wrong here
Shallower trees have higher bias.
- ✓
Increase model complexity
Why this is correct
More complex models can capture underlying patterns better, reducing bias.
- ✗
Add L1 regularization
Why it's wrong here
L1 regularization penalizes coefficients, potentially increasing bias.
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Last reviewed: Jun 20, 2026
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