Question 968 of 1,672
MLS-C01 Modeling Practice Question
A data scientist is training a neural network on a dataset with 1 million images. The training loss decreases steadily but the validation loss starts to increase after 10 epochs. Which action should the scientist take to improve generalization?
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
✓
Implement early stopping
Increasing validation loss while training loss decreases indicates overfitting. Early stopping (Option A) halts training when validation loss stops improving, directly preventing overfitting. Option B (adding more layers) increases model capacity and typically worsens overfitting. Option C (reducing learning rate) might slow training but does not directly stop overfitting. Option D (increasing epochs) would continue training and likely worsen overfitting.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Implement early stopping
Why this is correct
Early stopping halts training when validation loss stops improving, preventing overfitting. This is the most direct solution.
- ✗
Add more layers to the network
Why it's wrong here
Adding more layers increases model capacity and often exacerbates overfitting.
- ✗
Reduce the learning rate
Why it's wrong here
Reducing the learning rate might help training stabilize but does not directly address the overfitting issue.
- ✗
Increase the number of epochs
Why it's wrong here
Increasing the number of epochs continues training and would likely worsen overfitting.
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Last reviewed: Jun 20, 2026
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