Question 1,518 of 1,672
MLS-C01 Modeling Practice Question
A data scientist is training a neural network for time series forecasting. The training loss decreases initially but then starts to increase after 20 epochs. Which action should the scientist take to address this?
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 based on validation loss
Early stopping monitors validation loss and stops training when it starts increasing, preventing overfitting. Option A is wrong because increasing dropout may help with overfitting but the immediate issue of increasing loss is better addressed by early stopping, and dropout alone doesn't stop training. Option B is wrong because increasing the learning rate can cause divergence, making the loss increase worse. Option D is wrong because adding more layers increases model capacity and typically worsens 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.
- ✗
Increase the dropout rate
Why it's wrong here
Dropout helps, but early stopping directly addresses the increasing validation loss.
- ✗
Increase the learning rate
Why it's wrong here
Increasing learning rate may cause the loss to increase further or diverge.
- ✓
Implement early stopping based on validation loss
Why this is correct
Early stopping halts training when validation loss stops improving, preventing overfitting.
- ✗
Add more layers to the network
Why it's wrong here
Adding layers increases model complexity and may exacerbate overfitting.
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Last reviewed: Jun 20, 2026
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