MLS-C01 Modeling Practice Question
A company uses Amazon SageMaker to train a linear regression model. After training, the model shows high bias on the training set. Which action is MOST likely to reduce bias?
⚠ Common exam trap
A common mix-up: candidates confuse high bias with high variance and incorrectly choose regularization or more data, which are solutions 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 indicates that the model is underfitting the training data, meaning it is too simple to capture the underlying patterns. Adding more features increases the model's capacity to learn complex relationships, directly addressing underfitting by reducing bias. In SageMaker, this can be done by engineering additional input columns or using feature transformations before training.
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 more features
Why this is correct
More features can capture patterns better.
- ✗
Collect more training data
Why it's wrong here
More data helps variance more than bias.
- ✗
Apply L2 regularization
Why it's wrong here
Regularization increases bias.
- ✗
Deploy the model to a larger instance
Why it's wrong here
Instance size does not affect model bias.
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