MLA-C01 ML Model Development Practice Question
A data scientist is using SageMaker built-in XGBoost algorithm for a regression problem. Which metric is most appropriate as the objective metric for hyperparameter tuning?
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
✓
RMSE
For regression tasks, RMSE is a common objective metric. AUC is for classification, F1 is for classification, and NDCG is for ranking.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
NDCG
Why it's wrong here
NDCG is for ranking problems.
- ✓
RMSE
Why this is correct
RMSE is appropriate for regression tasks.
- ✗
AUC
Why it's wrong here
AUC is used for binary classification, not regression.
- ✗
F1
Why it's wrong here
F1 score is for classification.
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