Question 1,077 of 1,672
MLS-C01 Modeling Practice Question
A data scientist is training a gradient boosting model using SageMaker. The model is overfitting to the training data. Which TWO actions can help reduce overfitting? (Choose 2)
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
✓
Increase the minimum child weight
Increasing the learning rate actually worsens overfitting; increasing max_depth increases model complexity. Reducing max_depth and increasing min_child_weight both regularize the model.
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 boosting rounds
Why it's wrong here
More rounds increase overfitting risk.
- ✗
Increase the learning rate
Why it's wrong here
Higher learning rate can lead to overfitting, not reduce it.
- ✓
Increase the minimum child weight
Why this is correct
Higher min_child weight requires more data to split, reducing overfitting.
- ✓
Reduce the maximum depth of trees
Why this is correct
Shallow trees are simpler and less prone to overfitting.
- ✗
Use a larger training dataset
Why it's wrong here
While more data helps, it is not a hyperparameter action.
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Last reviewed: Jun 20, 2026
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