Question 795 of 1,672
Which Hyperparameters Reduce Overfitting in Gradient Boosting?
A data scientist is tuning a gradient boosting model using Amazon SageMaker Automatic Model Tuning (AMT). Which THREE hyperparameters should the scientist consider tuning to reduce overfitting? (Select THREE.)
Quick Answer
The answer is gamma, learning rate (eta), and subsample. Gamma, or minimum loss reduction, directly controls the complexity of a tree by requiring a minimum reduction in loss for a split to occur, which prunes overly specific branches that memorize noise. The learning rate (eta) scales the contribution of each tree, and a lower learning rate forces the model to learn slowly, reducing the influence of any single tree and thereby lowering overfitting risk. Subsampling randomly selects a fraction of training data for each tree, introducing randomness that prevents the model from fitting the training set too perfectly. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of how gradient boosting overfitting reduction hyperparameters interact—a common trap is confusing max_depth with gamma, but gamma is the more direct regularization parameter for leaf splits. Remember the mnemonic "GLS" for Gamma, Learning rate, and Subsampling to recall the three key levers against overfitting.
⚠ Common exam trap
Watch out — candidates often assume all listed hyperparameters are equally effective for reducing overfitting, but the exam expects knowledge that subsample ratio and maximum depth are also valid regularization parameters, yet the question specifically selects min_child_weight, gamma, and learning rate as the three to focus on.
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
✓
Learning rate (eta)
Learning rate (eta) controls the contribution of each tree to the ensemble. A lower learning rate forces the model to learn more slowly, requiring more trees but reducing the risk of overfitting by preventing any single tree from having too much influence on the final prediction.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Subsample ratio
Why it's wrong here
Subsample also reduces overfitting but is not among the three selected.
- ✓
Learning rate (eta)
Why this is correct
Lower learning rate reduces overfitting.
- ✓
Minimum child weight (min_child_weight)
Why this is correct
Higher values prevent overfitting.
- ✓
Gamma (minimum loss reduction)
Why this is correct
Higher gamma makes the model more conservative.
- ✗
Maximum depth (max_depth)
Why it's wrong here
Reducing max depth helps, but not selected.
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Same concept, more angles
2 more ways this is tested on MLS-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist is tuning a gradient boosting model using Amazon SageMaker's Automatic Model Tuning (hyperparameter optimization). The objective metric is validation:auc. After 50 training jobs, the best model still has a validation AUC of only 0.65. The scientist suspects overfitting because the training AUC is 0.99. Which hyperparameter configuration is MOST likely to reduce overfitting?
hard- ✓ A.Increase lambda from 1 to 10
- B.Increase num_round from 100 to 500
- C.Increase max_depth from 6 to 12
- D.Increase subsample from 0.5 to 1.0
Why A: Increasing lambda (L2 regularization) from 1 to 10 adds a stronger penalty on the magnitude of leaf weights in the gradient boosting model. This directly reduces overfitting by discouraging the model from fitting noise in the training data, which is consistent with the observed gap between training AUC (0.99) and validation AUC (0.65). In XGBoost, lambda controls the L2 regularization term on weights, and a higher value forces the model to be simpler and more generalizable.
Variation 2. 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?
hard- A.Increase the subsample ratio of training data
- ✓ B.Decrease the learning rate and increase the number of rounds
- C.Increase the learning rate
- D.Increase the maximum depth of trees
Why B: 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.
Last reviewed: Jun 24, 2026
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