MLS-C01 Modeling Practice Question
Which TWO of the following are techniques used to reduce overfitting in a neural network?
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
✓
L2 regularization
Options C and D are correct. C (L2 regularization) is correct because it penalizes large weights, reducing model complexity and overfitting. D (dropout) is correct because it randomly drops units during training, preventing co-adaptation. A is wrong because increasing the number of layers increases model complexity, which can worsen overfitting. B is wrong because batch normalization helps training stability but does not primarily reduce overfitting. E is wrong because increasing the learning rate may cause divergence, not reduce 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
Increasing the number of layers adds depth, which increases model capacity and typically exacerbates overfitting by enabling the network to memorise training noise. This option is tempting because adding layers is a standard technique for improving representational power when underfitting is the problem, such as when training loss fails to decrease sufficiently on a complex dataset.
- ✗
Batch normalization
Why it's wrong here
Batch normalization helps training stability but does not primarily reduce overfitting.
- ✓
L2 regularization
Why this is correct
L2 regularization penalizes large weights.
- ✓
Dropout
Why this is correct
Dropout randomly drops units during training, reducing overfitting.
- ✗
Increase the learning rate
Why it's wrong here
Increasing learning rate may cause divergence.
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