MLS-C01 Modeling Practice Question
A data scientist is using Amazon SageMaker to train a linear regression model. The training data contains 100 features and 1 million rows. The scientist notices that the model is overfitting, with training R² of 0.99 and validation R² of 0.65. The scientist has already tried adding L2 regularization and reducing the number of features. Which additional technique should the scientist try to reduce overfitting?
⚠ Common exam trap
It's easy for candidates to confuse techniques that improve optimization (batch size, learning rate) with techniques that improve generalization (more data, stronger regularization), leading them to pick B or C instead of A.
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
✓
Increase the amount of training data
Increasing the amount of training data provides the model with more examples of the underlying distribution, which helps reduce variance and combat overfitting. With 1 million rows and 100 features, the model may still be memorizing noise; adding more diverse data forces the linear regression to generalize better, improving validation R² without changing the model's capacity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Increase the amount of training data
Why this is correct
More data helps the model generalize better.
- ✗
Increase the batch size
Why it's wrong here
Larger batch size can lead to sharper minima and may not reduce overfitting.
- ✗
Increase the learning rate
Why it's wrong here
Higher learning rate may cause divergence, not reduce overfitting.
- ✗
Add more features
Why it's wrong here
More features increase model complexity and may worsen overfitting.
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