MLS-C01 Modeling Practice Question
Which THREE techniques can help reduce overfitting in a neural network trained on a small dataset?
⚠ Common exam trap
The MLS-C01 exam often tests the misconception that increasing model complexity (more layers or epochs) always improves performance, when in fact on small datasets it reliably worsens overfitting.
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 L2 weight regularization
L2 weight regularization (also known as weight decay) penalizes large weights by adding a term to the loss function proportional to the sum of squared weights. This forces the network to learn simpler patterns and reduces sensitivity to noise in the training data, which is especially helpful when the dataset is small and prone to 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.
- ✓
Apply L2 weight regularization
Why this is correct
L2 regularization penalizes large weights.
- ✗
Increase the number of hidden layers
Why it's wrong here
More layers increase model complexity and overfitting.
- ✗
Train for more epochs
Why it's wrong here
More epochs can lead to overfitting.
- ✓
Use data augmentation
Why this is correct
Data augmentation increases effective training size.
- ✓
Add dropout layers
Why this is correct
Dropout reduces co-adaptation of neurons.
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