MLS-C01 Modeling Practice Question
Which THREE techniques are effective for reducing overfitting in a deep 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
✓
Early stopping
Dropout (C) randomly drops neurons during training, forcing the network to learn redundant representations and reducing overfitting. L2 regularization (E) adds a penalty on large weights, discouraging complex models. Early stopping (B) monitors validation loss and halts training before the model starts overfitting. Increasing model complexity (A) would worsen overfitting, and reducing training data (D) also increases overfitting risk due to less generalization. Thus, the correct techniques are B, C, and E.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increasing model complexity
Why it's wrong here
Increasing complexity increases overfitting.
- ✓
Early stopping
Why this is correct
Early stopping prevents overfitting by stopping training.
- ✓
Dropout
Why this is correct
Dropout prevents co-adaptation of neurons.
- ✗
Reducing the amount of training data
Why it's wrong here
Reducing data worsens overfitting.
- ✓
L2 regularization
Why this is correct
L2 penalizes large weights.
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