MLS-C01 Practice Question: Machine Learning Implementation and Operations
A team is using Amazon SageMaker to train a linear regression model on a dataset with 10 features. After training, they notice the model has high bias. 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
✓
Add polynomial features to capture non-linear relationships
High bias indicates underfitting, which can be reduced by adding more features or increasing model complexity. Option A reduces risk of overfitting, not bias. Option B increases regularization, which increases bias. Option C reduces data, potentially increasing 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.
- ✗
Increase the regularization parameter lambda
Why it's wrong here
More regularization increases bias.
- ✗
Add L2 regularization
Why it's wrong here
Regularization increases bias, not reduces it.
- ✗
Use a smaller training dataset
Why it's wrong here
Less data can increase bias.
- ✓
Add polynomial features to capture non-linear relationships
Why this is correct
Adding features increases model complexity, reducing bias.
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