MLS-C01 Modeling Practice Question
Which THREE techniques help reduce overfitting in a neural network? (Select THREE.)
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
✓
Dropout
Dropout randomly drops units during training, L2 regularization penalizes large weights, and early stopping halts training when validation error increases. Data augmentation can also help but is not listed. Batch normalization may help but primarily for training stability.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Dropout
Why this is correct
Dropout is a regularization technique that reduces overfitting.
- ✓
L2 Regularization
Why this is correct
L2 regularization adds penalty for large weights, reducing overfitting.
- ✗
Increasing the number of layers
Why it's wrong here
More layers increase model complexity, likely overfitting.
- ✗
Using a larger batch size
Why it's wrong here
Larger batch size can lead to sharper minima and sometimes overfitting.
- ✓
Early Stopping
Why this is correct
Early stopping prevents overfitting by stopping before convergence.
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