MLS-C01 Modeling Practice Question
A data scientist is using Amazon SageMaker to train a linear regression model. After training, the scientist notices that the model has a high bias. What is the most likely cause?
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
✓
The model is too simple and underfits the data
High bias indicates that the model is underfitting the training data, meaning it is too simple to capture underlying patterns. Option D correctly identifies this cause. Option A is incorrect because too many features typically lead to high variance (overfitting), not high bias. Option B is incorrect because overfitting is associated with high variance, not high bias. Option C is incorrect because while an excessively high regularization parameter can increase bias, it is less likely than the model being too simple; regularization is designed to prevent overfitting, and its improper tuning is not the most common cause of high 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.
- ✗
The training dataset has too many features
Why it's wrong here
More features can reduce bias if relevant.
- ✗
The model is too complex and overfits the data
Why it's wrong here
Overfitting causes high variance, not high bias.
- ✗
The regularization parameter is too high
Why it's wrong here
High regularization increases bias, but the primary cause of high bias is model simplicity.
- ✓
The model is too simple and underfits the data
Why this is correct
Linear regression can underfit if relationship is nonlinear.
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