MLS-C01 Modeling Practice Question
A machine learning engineer is using SageMaker to train an XGBoost model on a dataset with a severe class imbalance (1:1000). The goal is to maximize recall on the minority class. Which hyperparameter tuning strategy is MOST appropriate?
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
✓
Set scale_pos_weight to the ratio of negative to positive samples
XGBoost's 'scale_pos_weight' parameter can be set to the ratio of negative to positive instances to help the model focus on the minority class. Adjusting max_delta_step or subsample may help but are secondary. Setting objective to 'binary:logistic' is default, not addressing imbalance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Set max_delta_step to a high value
Why it's wrong here
max_delta_step is for conservative updates, not directly for imbalance.
- ✗
Increase subsample ratio to 1.0
Why it's wrong here
Higher subsample may lead to overfitting, not specifically help imbalance.
- ✓
Set scale_pos_weight to the ratio of negative to positive samples
Why this is correct
This parameter adjusts the weight of the minority class, improving recall.
- ✗
Set objective to 'binary:logistic' and tune max_depth
Why it's wrong here
This does not directly address class imbalance.
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