MLS-C01 Modeling Practice Question
A data scientist is using Amazon SageMaker to train a linear regression model. The training data has 10 features and 100,000 observations. The model's training loss is decreasing, but the validation loss starts increasing after a few epochs. Which step should the data scientist take first to address this issue?
⚠ Common exam trap
Many exam-takers confuse overfitting with underfitting and incorrectly choose to add more features or increase epochs, not realizing that the validation loss increase is a direct sign of overfitting that requires reducing model capacity or learning rate.
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
✓
Reduce the learning rate
The increasing validation loss while training loss decreases is a classic sign of overfitting. Reducing the learning rate (Option B) is the first step to stabilize training by allowing the optimizer to take smaller, more controlled steps, which can help the model converge to a better local minimum and reduce validation loss. In SageMaker, this is typically adjusted via the `learning_rate` hyperparameter in the estimator.
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 to the model
Why it's wrong here
Adding features increases complexity and may worsen overfitting.
- ✓
Reduce the learning rate
Why this is correct
Reducing the learning rate can help the model converge more stably and reduce overfitting.
- ✗
Increase the batch size
Why it's wrong here
Increasing batch size can reduce noise but is not the most direct step for overfitting.
- ✗
Increase the number of epochs
Why it's wrong here
More epochs would likely increase overfitting.
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