MLS-C01 Modeling Practice Question
Which TWO actions can help reduce overfitting in a neural network? (Choose 2.)
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
✓
Apply L1 or L2 regularization.
Options C and E are correct because L1/L2 regularization penalizes large weights to prevent overfitting, and dropout randomly deactivates neurons to reduce co-adaptation. Option A is incorrect because adding layers increases model complexity, which can worsen overfitting. Option B is incorrect because decreasing the learning rate only affects training speed, not overfitting. Option D is incorrect because, although increasing dataset size can help reduce overfitting, it is not one of the two actions specified; L1/L2 regularization and dropout are the correct choices.
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 complexity, potentially increasing overfitting.
- ✗
Decrease the learning rate.
Why it's wrong here
Learning rate tuning affects convergence but not overfitting directly.
- ✓
Apply L1 or L2 regularization.
Why this is correct
Regularization penalizes large weights, reducing overfitting.
- ✗
Increase the training dataset size.
Why it's wrong here
More data helps generalization but not directly a regularization technique.
- ✓
Add dropout layers.
Why this is correct
Dropout reduces overfitting by randomly dropping neurons.
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