MLS-C01 Modeling Practice Question
A data scientist is training a convolutional neural network (CNN) for image classification using Amazon SageMaker. The training loss decreases steadily but validation loss starts increasing after a few epochs. Which action should the data scientist take to address this issue?
⚠ Common exam trap
Many candidates confuse overfitting with underfitting and choose to increase model complexity (Option B) or learning rate (Option A), not recognizing that rising validation loss signals the need to stop training rather than continue with more capacity.
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
✓
Implement early stopping based on validation loss
The described behavior—training loss decreasing while validation loss increases—is a classic sign of overfitting. Early stopping monitors the validation loss and halts training when it stops improving (or starts to increase), preventing the model from memorizing noise in the training data. In SageMaker, this can be implemented using the `EarlyStopping` callback in the framework's estimator or by setting `use_early_stopping` to True in a built-in algorithm.
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 learning rate
Why it's wrong here
Higher learning rate can cause divergence and overfitting.
- ✗
Add more convolutional layers
Why it's wrong here
More layers increase model capacity and overfitting.
- ✗
Increase the batch size
Why it's wrong here
Larger batch size can lead to sharp minima and overfitting.
- ✓
Implement early stopping based on validation loss
Why this is correct
Early stopping prevents overfitting by stopping training when validation loss plateaus or increases.
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