MLS-C01 Practice Question: Machine Learning Implementation and Operations
A startup is using SageMaker to train a model using the built-in XGBoost algorithm. The training job runs successfully but the resulting model performs poorly on the test data. The data scientist suspects overfitting. The training data is relatively small (10,000 rows). Which action should be taken to reduce overfitting?
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
✓
Decrease the number of trees (num_round) to 50
Overfitting occurs when the model learns noise in the training data. Decreasing the number of trees (num_round) reduces model complexity, which helps prevent overfitting, especially with a small dataset (10,000 rows). Option B (increasing learning rate) can cause the model to converge too quickly to a suboptimal solution, potentially increasing overfitting. Option C (increasing trees) increases complexity and overfitting. Option D (larger instance) does not affect overfitting as it only changes compute resources.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Decrease the number of trees (num_round) to 50
Why this is correct
Fewer trees reduce overfitting.
- ✗
Increase the learning rate to 0.3
Why it's wrong here
Higher learning rate may cause overfitting or instability.
- ✗
Increase the number of trees (num_round) to 500
Why it's wrong here
More trees increase overfitting.
- ✗
Use a larger instance type
Why it's wrong here
Does not affect overfitting.
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