MLS-C01 Modeling Practice Question
A company is training a deep learning model on Amazon SageMaker. The training job is taking a long time and the data scientist suspects that the model is overfitting. Which of the following actions can help reduce overfitting and improve generalization?
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
✓
Add dropout layers to the model architecture.
Adding dropout layers is a regularization technique that randomly drops neurons during training to prevent overfitting. Increasing the number of epochs (Option B) would likely worsen overfitting. Using a larger batch size (Option C) can sometimes help generalization but is not a direct regularization technique. Removing regularization (Option D) would increase 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 batch size used during training.
Why it's wrong here
Larger batch sizes can sometimes improve generalization but are not a primary method to reduce overfitting.
- ✓
Add dropout layers to the model architecture.
Why this is correct
Dropout is a regularization technique that helps prevent overfitting by randomly dropping neurons during training.
- ✗
Increase the number of training epochs.
Why it's wrong here
Increasing epochs typically leads to more overfitting, not less.
- ✗
Remove regularization terms from the loss function.
Why it's wrong here
Removing regularization would likely increase overfitting.
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