MLS-C01 Modeling Practice Question
A data scientist is tuning a gradient boosting model using Amazon SageMaker Automatic Model Tuning. The objective metric is AUC. The training job converges quickly but the final model has low AUC on the validation set. Which hyperparameter should the data scientist adjust to improve validation AUC?
⚠ Common exam trap
The trap here is that candidates mistakenly think increasing the learning rate will speed up convergence and improve AUC, but in reality it causes overfitting when the model already converges quickly, while decreasing the learning rate with more rounds is the standard remedy for underfitting or overfitting in gradient boosting.
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 learning rate and increase the number of rounds
Decreasing the learning rate and increasing the number of rounds is the correct approach because a low learning rate forces the model to take smaller steps toward the optimum, reducing overfitting and allowing more trees to contribute to the ensemble. This combination often improves generalization and validation AUC when the training job converges too quickly, indicating that the model is overfitting or underfitting due to aggressive learning.
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 subsample ratio of training data
Why it's wrong here
Increasing subsample may help but is less impactful than learning rate adjustment.
- ✓
Decrease the learning rate and increase the number of rounds
Why this is correct
Lower learning rate with more rounds typically improves generalization and AUC.
- ✗
Increase the learning rate
Why it's wrong here
Higher learning rate can cause overfitting and reduce validation AUC.
- ✗
Increase the maximum depth of trees
Why it's wrong here
Deeper trees increase overfitting risk.
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