MLA-C01 ML Model Development Practice Question
A data scientist is using SageMaker to train an XGBoost model for a regression problem. After training, they evaluate the model on a test set and get an RMSE of 10 and an R² of 0.85. Which additional metric would give the MOST insight into the model's average prediction error magnitude?
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
✓
Mean Absolute Error (MAE)
MAE (Mean Absolute Error) gives the average absolute prediction error, which is easy to interpret in the same units as the target. RMSE gives a similar but squared metric, and R² indicates variance explained, but MAE directly answers the average error magnitude.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
AUC
Why it's wrong here
AUC is for binary classification.
- ✗
Confusion matrix
Why it's wrong here
Confusion matrices are for classification.
- ✗
F1 score
Why it's wrong here
F1 is a classification metric, not suitable for regression.
- ✓
Mean Absolute Error (MAE)
Why this is correct
MAE provides the average absolute difference between predictions and actuals, directly indicating average error magnitude.
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