MLS-C01 Modeling Practice Question
A data scientist is tuning a gradient boosting model using SageMaker automatic model tuning. The hyperparameter 'num_round' ranges from 50 to 500. The tuning job uses 'ObjectiveMetric' = 'validation:auc'. After 50 training jobs, the best objective value is 0.95. The data scientist suspects overfitting. What should the data scientist do?
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
✓
Add an early stopping round and increase the range for regularization hyperparameters like 'gamma' and 'lambda'.
Adding an early stopping round prevents training after validation performance stops improving, and increasing the range of regularization hyperparameters like 'gamma' (minimum loss reduction) and 'lambda' (L2 regularization) helps penalize overly complex models, reducing overfitting. Option A (increasing 'max_depth') would allow deeper trees that can memorize noise, worsening overfitting. Option C (increasing 'num_round' to 1000) with no regularization and no early stopping would likely lead to further overfitting. Option D (decreasing 'num_round' to 10-100) might underfit, but it does not address the root cause of overfitting and could reduce performance.
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 'max_depth' to capture more complex patterns.
Why it's wrong here
Increasing depth typically increases overfitting.
- ✓
Add an early stopping round and increase the range for regularization hyperparameters like 'gamma' and 'lambda'.
Why this is correct
Early stopping prevents overfitting; regularization penalizes complexity.
- ✗
Increase 'num_round' to 1000 and keep other hyperparameters unchanged.
Why it's wrong here
More rounds without regularization likely worsens overfitting.
- ✗
Decrease the range of 'num_round' to 10-100.
Why it's wrong here
Reducing rounds may underfit; regularization is better.
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