MLS-C01 Early stopping Practice Question
Which THREE of the following are valid strategies to reduce overfitting in a deep neural network? (Choose 3)
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
✓
Use early stopping.
Early stopping halts training when validation performance degrades, preventing overfitting. Option D is correct because L2 regularization adds a penalty on large weights, discouraging complexity. Option E is correct because dropout randomly drops neurons during training, reducing co-adaptation. Option A is wrong because adding more layers increases model capacity, which exacerbates overfitting. Option C is wrong because a higher learning rate can cause the loss to diverge and does not directly address 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 number of layers.
Why it's wrong here
More layers increase capacity and overfitting.
- ✓
Use early stopping.
Why this is correct
Early stopping prevents overfitting.
- ✗
Increase the learning rate.
Why it's wrong here
Higher learning rate does not reduce overfitting.
- ✓
Add L2 regularization to the loss function.
Why this is correct
L2 regularization reduces model complexity.
- ✓
Use dropout layers.
Why this is correct
Dropout is a regularization technique.
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