MLS-C01 Modeling Practice Question

A data scientist is training a binary classification model using SageMaker XGBoost and notices that training loss decreases but validation loss increases after a few epochs. Which action should the data scientist take to address this issue?

⚠ Common exam trap

AWS often tests the misconception that increasing model complexity (more rounds, deeper trees, higher learning rate) always improves performance, when in fact these actions worsen overfitting when validation loss diverges from training loss.

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

✓

Set early stopping based on validation loss

The increasing validation loss while training loss decreases is a classic sign of overfitting. Setting early stopping based on validation loss halts training when the validation loss stops improving, preventing the model from memorizing noise in the training data. SageMaker XGBoost's `early_stopping_rounds` parameter monitors the evaluation metric on the validation set and stops training if no improvement is seen for a specified number of rounds.

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 number of rounds
Why it's wrong here
Would continue overfitting.
✓
Set early stopping based on validation loss
Why this is correct
Stops training when validation loss stops improving.
✗
Increase the learning rate
Why it's wrong here
May cause divergence.
✗
Increase the maximum tree depth
Why it's wrong here
Would increase overfitting.

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MLS-C01 Modeling Practice Question

Why each option matters

Option-by-option breakdown

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