MLS-C01 Modeling Practice Question
A company uses Amazon SageMaker to train a linear regression model. The training data includes a feature 'age' with values ranging from 0 to 100. The model's loss is not converging. 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 features are not normalized.
Normalizing features (e.g., scaling 'age' to a similar range as other features) is critical for linear regression convergence. With 'age' ranging 0-100, if other features are in smaller ranges, the optimizer may struggle. Option B (outliers) can cause issues but is less likely than lack of normalization. Option C (instance type too small) typically affects training time, not convergence. Option D (learning rate too high) can cause divergence, but normalization is a more common first step.
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 features are not normalized.
Why this is correct
Unscaled features cause gradient descent to oscillate.
- ✗
There are outliers in the target variable.
Why it's wrong here
Outliers affect fit but not necessarily convergence.
- ✗
The instance type is too small.
Why it's wrong here
Instance size usually does not prevent convergence.
- ✗
The learning rate is too high.
Why it's wrong here
While possible, feature scaling is more likely the issue.
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