MLS-C01 Modeling Practice Question
A team is training a linear regression model to predict house prices. After training, they observe that the model has high bias (underfitting). Which action is most likely to reduce bias?
⚠ Common exam trap
Many candidates confuse regularization (which controls overfitting) with bias reduction, mistakenly thinking increasing regularization or reducing parameters will fix underfitting, when in fact those actions increase 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
✓
Add more relevant features and increase model complexity.
High bias (underfitting) means the model is too simple to capture the underlying patterns in the data. Adding more relevant features and increasing model complexity (e.g., using polynomial features or more interaction terms) gives the linear regression model greater capacity to fit the training data, directly reducing bias. This aligns with the bias-variance tradeoff, where increasing complexity lowers bias at the cost of potentially increasing variance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the regularization strength.
Why it's wrong here
Increasing regularization increases bias.
- ✗
Reduce the amount of training data.
Why it's wrong here
Reducing data can increase bias.
- ✗
Decrease the number of model parameters.
Why it's wrong here
Decreasing parameters reduces complexity and increases bias.
- ✓
Add more relevant features and increase model complexity.
Why this is correct
Adding features reduces bias.
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