MLS-C01 Modeling Practice Question
Which THREE techniques can help reduce overfitting in a neural network? (Choose 3)
⚠ Common exam trap
The MLS-C01 exam often tests the misconception that increasing model capacity (e.g., more layers) or adjusting the learning rate can reduce overfitting, when in fact these techniques either exacerbate overfitting or address convergence issues rather than regularization.
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 is a regularization technique that randomly drops a fraction of neurons during training, which prevents the network from relying too heavily on any single neuron and forces it to learn more robust features. This reduces overfitting by introducing noise that improves generalization.
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 randomly drops neurons, reducing overfitting.
- ✗
Increasing the number of layers
Why it's wrong here
More layers increase capacity and overfitting.
- ✗
Using a larger learning rate
Why it's wrong here
Larger learning rate may cause divergence.
- ✓
Early stopping
Why this is correct
Stops training when validation loss increases.
- ✓
L2 regularization
Why this is correct
L2 penalty reduces weights, preventing overfitting.
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