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?
⚠ Common exam trap
The trap is confusing classification metrics (AUC, F1) with regression metrics; MLA-C01 often tests whether candidates know that RMSE is for regression while AUC and F1 are for classification.
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
RMSE (Root Mean Squared Error) is the most appropriate objective metric for hyperparameter tuning in a regression problem because it directly measures the average magnitude of prediction errors, with larger errors penalized more heavily. SageMaker's built-in XGBoost algorithm supports RMSE as an evaluation metric for regression, and it is commonly used as the objective metric for tuning jobs.
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 measures graded relevance ranking, so it cannot evaluate a continuous regression target; SageMaker's XGBoost regression objective minimises RMSE or MAE instead. It is tempting because NDCG is the built-in objective for ranking tasks, and would be the correct choice when tuning a learning-to-rank model that orders documents or products by relevance.
- ✓
RMSE
Why this is correct
RMSE is the standard objective metric for regression with XGBoost, measuring root mean squared prediction error in the target's units. SageMaker hyperparameter tuning minimises it, directly reflecting the regression task's accuracy, unlike classification metrics such as accuracy, F1, or AUC.
- ✗
AUC
Why it's wrong here
AUC measures ranking discrimination for binary classification, so it cannot evaluate a continuous regression target. It tempts because SageMaker's XGBoost exposes AUC for classification jobs, where it would be the right objective. For regression, tuning must minimise RMSE or MAE against predicted numeric values.
- ✗
F1
Why it's wrong here
F1 scores classification precision and recall, so it cannot be computed from the continuous residuals XGBoost's regression objective produces. It is tempting because F1 is the standard objective metric for imbalanced binary classification, where it would correctly be chosen over accuracy.
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