MLS-C01 Modeling Practice Question
A data scientist wants to use a linear regression model to predict house prices. After training, the model shows high bias and low variance. Which action would most likely improve the model's performance?
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 polynomial features to capture non-linear relationships.
High bias indicates underfitting, meaning the model is too simple to capture underlying patterns. Adding polynomial features increases model complexity, allowing it to better fit the training data and reduce bias. Option B is incorrect because increasing L2 regularization strength penalizes large coefficients, which increases bias and makes underfitting worse. Option C is incorrect because using a simpler model (e.g., linear regression without interaction terms) would further increase bias. Option D is incorrect because reducing training data does not address bias; it can increase variance, but bias remains high.
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 polynomial features to capture non-linear relationships.
Why this is correct
Increasing model complexity reduces bias by better fitting the data.
- ✗
Increase L2 regularization strength.
Why it's wrong here
Adding L2 regularization increases bias, worsening high bias.
- ✗
Use a simpler model, such as linear regression without interaction terms.
Why it's wrong here
Simplifying the model further by removing interaction terms would reduce the model’s ability to capture existing patterns, thereby increasing bias — exactly opposite to what is needed for a model already suffering from high bias (underfitting). This action is tempting because removing complexity correctly addresses high variance (overfitting) in a different scenario where the model is too flexible.
- ✗
Reduce the amount of training data.
Why it's wrong here
Reducing training data may increase variance but does not directly reduce bias.
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