MLS-C01 Modeling Practice Question
A data scientist is deploying a regression model in Amazon SageMaker that predicts housing prices. The model shows high bias (underfitting). Which action is most likely to reduce bias?
⚠ Common exam trap
Many exam-takers confuse bias with variance and incorrectly choose regularization or simpler models, which are solutions for overfitting (high variance), not underfitting (high 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 features or increase model complexity
High bias (underfitting) means the model is too simple to capture the underlying patterns in the data. Adding more features or increasing model complexity (e.g., using polynomial features, deeper trees, or a more flexible algorithm) directly addresses underfitting by giving the model greater capacity to learn from the data. In Amazon SageMaker, this could involve using a more complex built-in algorithm like XGBoost with deeper trees or adding feature engineering transformations in a processing job.
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 amount of training data
Why it's wrong here
Less data increases bias.
- ✗
Increase regularization strength
Why it's wrong here
Regularization increases bias.
- ✗
Use a simpler model
Why it's wrong here
Simpler model increases bias.
- ✓
Add more features or increase model complexity
Why this is correct
More complex models can capture patterns better.
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